I feel there's a disconnect between trends in kids' and teenagers' interests and skills as analyzed and reported by teachers and other people who work with kids and teenagers extensively, how those reports are read by adults who DON'T work with kids and teenagers other than perhaps their own, and how those reports are interpreted by the kids and teenagers.

I can't help but suspect that this is a major factor at the core of the perennial problem of generational disconnect.

For example, let's look at the declining rate of casual PC usage and basic PC skills.

What a teacher might say: "I teach a high school class using xyz computer software and it's worrying me that over the last several years, out of every class, there have been at least a small handful of students who don't know how folders work, or how to use a physical keyboard, or who send cell phone photos of their screens as "screenshots", and hell, some don't even know the difference between a laptop and a desktop computer. This wasn't nearly as common ~10 years ago. The system for the years before mine used to teach more of the basics, and now I'm seeing evidence that just expecting people to get it on their own isn't working, and that's a problem."

The reality that this statement is about: A decline from a basic computer literacy rate of (just as an example, absolutely not to be taken as an objective fact) 90% to 80% and even sharper at intermediate to advanced levels, starting with the most underprivileged, in a world where PC usage is still critical for a huge chunk of the professional world, is a VERY bad sign. It represents technical knowledge becoming more and more of a class divider, in a way that has the potential to snowball. We're still in early stages, and it's FAR from being the fault of the ~10% of kids who would have been taught computer basics if they'd been born 10-15 years prior, but it IS real and it shows that we need to make formal classes in PC basics more normal and accessible again, instead of just expecting people to pick it up by osmosis, because that experiment isn't working.

What entirely too many adults hear: Generations Z and alpha are stupid spoiled idiots about technology who don't know computer, they only know how to app store, TikTok, selfie, eat hot chip, and lie! Which is their own fault, obviously. If they just paid attention to their teachers instead of Instagram and Twitter everything would be fine!

What teenagers hear: Man, adults just loooove to look at the teenagers who are doing the worst and make shit up about the downfall of society or whatever, meanwhile all my friends and I know how to use a computer, the only problem is a bunch of old fuddy-duddies talking shit about how back in their day they had to walk 15 miles in the snow uphill both ways just to go to the bathroom, AGAIN.

Because it's hard to see the a pattern like this, especially in fairly early stages, as a matter of statistics. Humans kind of suck at intuiting statistics. We want hard and fast rules. As far as our brains often see it, anything with a probability over 50% is a certainty; anything under 50% is an impossibility. If you're in a room of 10 people, and you ask who doesn't know the basics of a computer, it won't make much difference whether one person raises their hand, or two - either way, if you throw a paper ball at a random person, you're far more likely to hit someone who can install a program than someone who can't. Meanwhile, if you ask all the people in several of those 10-person groups who raised their hands to go to another room, and you see twice as many people as 5 years ago, it's easy to think that NO ONE knows how to use a computer anymore. Whichever side you look at, it's black and white. Either nearly everyone knows how to use a computer, or nearly no one does; it's black and white. Easy numbers. Comfortable.

So far distorted from the realities that created the numbers that it might as well be from an alien planet.

And thus, not only do a lot of people end up not seeing the problem for what it is, but people just end up having pointless fights over which of those black-and-white views is correct, because according to immediate intuitive monkey-brain, it CAN'T be somewhere in the middle. It's very hard to truly, deeply recognize the fact that "most young people still have basic computer literacy" and "the number of young people who DON'T have the skills they need to compete in a tech-oriented professional world is increasing at an alarming rate" can both be true statements at the same time.

Now let's just ask ourselves, how many OTHER trends and shifts across generations have we fallen into the trap of talking about like this?

Vasily's Literacy

I’ve been asked a couple times about Vasily’s literacy, so here’s a quick overview of stats and pertinent information laid out regarding that idea. I’ll make my statement on my own opinion towards his literacy at the end, but for now I’ll focus on exact numbers and stats without interjecting opinion.

While this post primarily pulls specific stats from When Russia Learned to Read by Jeffery Brooks, I’ve read a couple papers and other books regarding these subjects I pull general statements from as well. Please note, though, that there were no large scale official census in Russia until 1897 so some of these stats from previous years could potentially be skewed.

Source Material

First and foremost: do we ever see Vasily read or write in the series?

No. However, this can be explained quickly away. Until running into Tsukishima, Vasily was likely (incorrectly) assuming that not only did none of the Japanese characters speak Russian, but they did not use the same writing system either. It would have been a waste of resources (paper) to attempt a conversation where a drawing could suffice. Additionally, there is no other scene in the series involving him and Tsukishima that would have warranted him writing to Tsukishima either. At least, none that we see. So, him never being seen writing does not necessarily prove he is illiterate.

Vasily’s Age

With that out of the way, there’s another important piece of information we need to pin about Vasily before we continue: Vasily’s age. While Noda specified he’s the same age as Ogata, Ogata is unfortunately given no age range. But, unlike these two, Usami does have a canon age: 26. We can use Usami as a frame of reference because Noda stated Usami is, in fact, older than Ogata. Thus, this means Ogata is 25 and so Vasily is also 25.

Now, I personally tend to make these two older, but for a frame of reference we are going to pin Vasily at 25 years old. The reason this is significant is to pin down exactly when Vasily went to school. If in 1907 he was 25, then the age he was deemed ‘school age’ (8 - 11) would be around 1890 - 1893. Of course, he always could have attended school at an earlier or later age, but for conjecture’s sake, we will use the average age such as these.

As I said previously, an official census was not published in Russia until 1897, but any previous information before that typically begins around the 1870s. So it would benefit us to default to 1897 statistics, but keep in mind that the stats are skewed a tad higher than they would have been.

Rural Literacy

Literacy in the late 19th century was not nearly as bad as people make it out to be (at a rate of around 21% in 1897), but only because rural numbers brought it down. For example, in industrialized cities such as Moscow, 70% of men were regarded as being literate. There were also a plethora of schools dotting the country, from Zemstvo-funded schools, to church schools, to state schools. It was often not the lack of schooling availability that caused a decrease in rural literacy compared to urban children, but rather social aspects.

I am unable to find exact stats for rural literacy rates around the year 1890, but literacy rose from 6% in rural populations in the 1860s to 25% in 1910. It’s also best to keep in mind, however, men were far more likely to be literate than women, and the young more likely than older populations as well. So, if we were to take an increased decade raise (rounding up to about 4% every 10 years), and exclude the population numbers from including women which will be about half the population, we get around a 36%* base chance Vasily is literate when he is from a rural population (of which he is – he is from Yeleninka, a rural town in the Orenburg voisko), still not excluding elderly populations.

This is still not a very high chance, but there’s some other factors to discuss. Firstly, would have to be involving his background. The reason literacy was so low in rural areas was because, although parents did place value on literacy because it allowed for social movement and potentially higher wages, parents simply could not afford the lost labor of their children attending school. If a family had several children and could afford the loss of labor, then a child was much more likely to attend school. So, even if Vasily’s family had been described as being poor by Noda, this had no bearing on Vasily’s likeness to attend school. Given Vasily is almost entirely assured to be in the military through conscription, he very likely had brothers. And if he had brothers – he was very likely to attend school compared to single children families.

Another factor involves his family’s occupation. Families dependent on agricultural work were less likely to send their children to school because it was expected for them to work on that same farm when they were older, thus limiting their need for literacy. But, if Vasily’s family were artisans or practiced some kind of craft alongside agriculture, parents highly valued literacy in comparison, and were more willing to spare the labor loss for schooling.

Religion also played a role. Specifically, those of the Old Believer faith tended to be more literate and push to educate their children regardless of their occupational status compared to regular Russian Orthodox peasants. Aside from a general cultural insistence on preferring literacy, there is no other reason why this occurred, as the only major difference between Old Believers and Russian Orthodox peasants was a matter of ceremony (excluding some fringe Old Believer cults). If Vasily came from an Old Believer family, they'd push for him to be literate regardless of the labor loss they'd experience.

Finally, some parents preferred to send their sons to school to lower their military conscription length. While university students conscripted only had to serve 1.5 years of the required 5 year length, those who completed at least 3 years of any schooling had that length lowered to 4 years. If a family had several sons, which meant their sons were eligible to be drafted by the lotto, they would be more partial to educating said sons.

For some stats: unfortunately I could only find the rate of attendance of boys in school for 1911. Please examine these stats with a critical eye that they should be lower. 88% of boys in rural areas attended school for at least 1 year, but by year 3 this percentage dropped to about 38.5%.

*My math numbers will be off because there were a decent amount of women who were literate, just at a noticeably lower rate compared to men. For ease of math’s sake, I removed them from the population entirely, though the original percentage statistic did include them. They originally were likely less than 1% of the literate population in the 1860s statistic I used as a base.

Soldier Literacy

While it is useful to look at literacy stats of Vasily’s background (being a rural resident), what’s more useful is the literacy rates of the army for when Vasily was serving.

By the 19th century, Russia realized the value in literate soldiers – but unfortunately for Vasily, schooling for soldiers that the government had originally created in 1855 was abolished across the 1890s. But this did not mean literacy still did not rise in the military, as certain soldier ‘uncles’ brought it upon themselves to educate other soldiers. In fact, literacy in the army rose from about 21% in 1874 to about 68% in 1913 – rounded up to about a 6% increase in literacy every 5 years. Vasily would’ve been conscripted into the army by 1902, and applying the rate of increase, there was about a 51% literacy of the army in 1899, and 57% literacy in 1904. A higher than half chance for Vasily, who we see actively still in the army by 1907.

There are other factors to consider as well: Vasily’s rank and station. While the illiterate often went to the infantry units, specialized units had much higher rates of literacy. As I’ve discussed in the past, technically Vasily was in a specialized unit – the Special Border Guard Corps. His literacy chance rises higher due to this factor, as literacy was especially preferred because of the ability to read topographic maps and telegrams.

It is not Vasily’s presence in the SBGC that also increased his likelihood of being literate – it was his rank as well. While Noda removed most telling marks from Vasily of his rank, such as shoulder straps, there’s two glaring tells. Firstly, are his and Ilya’s binoculars. Ilya appears to be to be a Feldwebel (equivalent to an American First-Sergeant, British Sergeant-Major) given his position of ordering the other soldiers, and that he has binoculars which were only used by officers. He is, like Tsukishima, a Non-Commissioned Officer (NCO). Vasily himself also has binoculars, though one could argue this does not inherently make him an NCO because Ilya has at least two traits marking him as an NCO. After all, Vasily could have stolen his binoculars and his overcoat is one that a private would wear (Ilya does wear a private’s coat as well. Though, I have addressed before that the uniforms of the border guards gang are completely incorrect regardless of rank, so I am unsure of how much weight this should be given).

That second tell is actually Vasily’s cockade. The cockade worn on the hats of soldiers denoted generally their rank and status. So, while Vasily lacks any other visual clothing tells, his cockade can give a general idea if he is of a lower or higher rank, which does indeed change his literacy statistics.

[Pictured are 3 cockades. The far left is the cockade of a lower rank soldier, while the cockades in the middle and far right are cockades of officers. Thank you to @rdstrpv for this image!]

This information is important because NCOs were almost demanded to be literate. It was essential for their occupation, as being able to read maps was one of the most important skills for an NCO to have. If Vasily was an NCO, which his cockade would indicate, he almost assuredly would be literate.

[Pictured are the 3 different ways Vasily’s cockade is drawn.]

While in the anime Vasily’s cockade is given the appearance of the average lower-rank soldier, and typically this is how people place him. However in the manga, Vasily’s cockade is more ambiguous. When referencing it to the cockades above, it could pass as both a lower-rank or an officer’s cockade. The final example is of Noda’s detailed Vasily illustration that was not outsourced by an animation studio, nor constrained by swift time spent on manga panels. In this, Vasily clearly has on the cockade of an officer.

Obviously, given the anime drawing Vasily with a lower-ranking cockade and the manga is ambiguous, you could still make the argument he’s a low ranking soldier. Nonetheless, one should also consider that the government likely would’ve preferred to send a group of officers to apprehend a Tsar’s killer over, perhaps, privates, giving more credence to him being an NCO. And thus, Vasily’s literacy likeness goes up to almost-guaranteed. There were occasional examples of NCOs not being literate, but there were few and far inbetween, making it unusual that a young NCO was illiterate by 1907.

Cossack Literacy

Of course, Vasily was not necessarily in the SBGC. He could have instead been a Cossack. While the idea of an NCO and cockade still apply to Cossacks, I will still discuss Cossack literacy in the case you find Vasily to be of a lower rank.

Unfortunately exact statistical information regarding Cossack literacy has almost never been tracked before the Soviet period. Still, by the 19th century the Imperial Russian government had a special vested interest in educating their Cossacks, more so than their peasantry. There were many Cossack schools that taught everything from literacy to combat that children were almost required to attend. In fact, once entering the military at 21, Cossacks were required by the military to be literate unlike other soldiers, and if they were not literate they were mandated to pursue education while they were deployed.

This is not to say there were not illiterate Cossacks – one could finish their entire service as a Cossack without properly pursuing their literacy if they were crafty about it, similar to illiterate NCOs. But, again, this was unlikely. In comparison, Cossacks were far more likely to be able to read than that of the ordinary peasant in the army.

Final Thoughts

I’m of the opinion Vasily actually is literate, regardless of him being in the SBGC as an NCO or a Cossack. He’s a very prideful character, and it slowly became a limiting stigma that one was illiterate in Russia, even in 1907. This is not to say Vasily can’t be illiterate – many of the stats I gave showed that there was a decent chance for illiteracy, especially if he was a first-born son to a farming family and only low-ranked. But in my opinion of all the facts culminating, I find I prefer the idea of him being literate. Have fun with this information regardless, and may it help you in whatever you intend to write or draw in the future!

A big thank you to @rdstrpv for her help in answering a couple of my questions to make sure I wasn’t misrepresenting information, and for her images. She's always a big help.

that 88% statistic literally comes from a poll conducted in january by tel aviv university called the peace index survey. if you google anything along the lines of israeli support plus 88% the first result is a nyt article that cites it with a link to the study. it took literally two seconds to find. 88% of jewish israelis surveyed believe that the number of palestinian casualties, including both deaths and injuries, is justified. it also found 94% believe the idf has used adequate or too little force in the war (43% believe it’s too little!). this survey was taken at a time when 20000 palestinians had already been killed and much of gaza leveled 

unless you’re going to claim that tel aviv university is disreputable/has anti israeli bias/is antisemitic, this clearly shows overwhelming israeli support for the level of death and destruction in gaza. the fact that netanyahu is unpopular is completely irrelevant and i’m not even sure why that was included in your post as some sort of evidence that the 88% stat must be untrue. support for the war clearly goes far beyond just the right wing parties

also the fact that so many in the notes of that post talk about the importance of sources but made 0 effort to even look up the original statistic and claimed with 0 evidence that it was made up is. very telling.

Dude, I wrote in the post itself that I might have googled the wrong terms. I googled variatiojs "destruction in Gaza + 88%" instead, because that's how I saw it quoted. I went on a Google deep dive, and still left doubt in my post BECAUSE I COULD HAVE BEEN WRONG.

The post covered two different subjects because I wanted to talk about two different subjects - the fact that people don't understand how the Israeli government works, statistics regarding that, and that since I saw the statistic quoted multiple times unsourced, including in a professionally published piece, I decided to talk about statistics literacy in general. This isn't an academic article - it's my own personal blog.

Also, chill. I cited multiple sources ranging a wide range of subjects, and didn't even conclude that Israelis don't support the destruction in Gaza at all.

Now I have found the source you're referring to and am looking at it. It covers a lot of questions, such as a majority doubting that the war in Gaza even has a purpose (52%). You're right on the 94% percent and the way it's divided.

I however once again point out that the (actually slightly less, but that's frankly irrelevant, I'm just a pedant) 88% you're citing IS DIVIDED INTO TWO CAMPS. Relatively justified is 21.3%, which still has doubts.

I also clarified again and again in my original post and in other posts *that I'm opposed to the war in Gaza*. I've also clarified that I think that people on both sides of the conflict are people even if they have opinions that I find horrific, such as MOST OF THE OPINIONS I cited. My point, overall, was to view people complexly.

Support for Hamas has increased in Gaza and more than trippled in the West Bank since the war started. Does that mean that Palestinians are all evil? That I should avoid viewing them complexly, say they should all die, call for a second nakba? Because I see people on the website and irl calling all Israelis (and really all Jews) evil, saying they should all die, and calling for another intifada.

My post, same as everything else I post, talks about viewing all people as people. And also about citing your sources and showing how I cross reference multiple sources, not that I suppose that matters to you.

I think it's very telling you're on anon, and I think I know the kind of content I'd see on your blog. And that's the last thing I'll say on that.

Hello sorry to disturb you. In a post from some time ago (about Okada Izo and literacy) you talked about how at the end of the Edo era there were books printed with kana targeted toward women and children. You linked a (very interesting) article about Female Readership in Edo in this post. The article gives many interesting details about books and their female readership, but I am not sure about the average level of literacy of women at the time. The only thing I am sure of is that higher class women were more literate than lower class women, and men tended to be more literate than women of similar social classes. Would an middle class woman (from a low samurai family, or a not particularly rich merchant family, but not poor) know how to read kanji at the time?

Questions are never a bother as long as you don't mind that I sometimes just don't have an answer, and that other times it takes a long time before I can get to it.

But I do have a quick answer for this question because it came with perfect timing: I just read an article on Edo period literacy by Tsujimoto Masashi!

Estimating an average literacy rate for the Edo period is difficult because the rate varied between the early, middle and late Edo period, between urban and rural settings, and because research in this area suffers from an overall lack of accurate period statistics.

That said, Tsujimoto estimates that by the mid-1700s, the literacy rate in Japan's three largest cities - Edo, Kyoto, and Osaka - may have been as high 60-70% among males and 40-50% among females, and that literacy rates in rural areas were likely about 10-20% lower than this.

Being literate in Edo period Japan was a near necessity because not being literate put you at significant disadvantage: edicts and important information was posted on public signboards, and the government accepted no reports, petitions, or appeals that were not presented in writing.

Even in villages, where the overall level of literacy may have been low, village officials and wealthy farmers needed to be both literate and adept at math to ensure records were kept and were kept accurately.

Schools for the common people, such as merchants, townspeople, and farmers, were not regulated by the shogunate, so anyone with an inclination to teach could open a school of their own. This was often a go-to for unemployed samurai, but as Tsujimoto points out in his article, it was also a common way for widows in urban areas to make their living as writing teachers.

How common were those schools? Tsujimoto points to a 1722 letter written by a Confucian scholar employed by the shogunate, which states that "there are more than 800 writing teachers in Edo". That's roughly one writing teacher per residential block in the areas of the city inhabited by townspeople rather than samurai.

Tsujimoto writes: "The study of penmanship usually began with the iroha (that is, kana). Then, after studying numbers, students learned the Chinese characters for things familiar from everyday life, personal names, place names, and the heavenly directions. Next, they progressed to a text that was a compilation of phrases commonly used in letters, idiomatic expressions, and standard sentences."

As students usually started their studies around age 6 or 7, and generally continued for about 5 years, this meant most students who attended writing schools, both male and female, would have had a good foundation in kana, as well as knowledge of at least a fair number of commonly-used kanji.

Therefore: a woman from a poor samurai family or an average merchant family had a good chance of being able to read and write at least a limited number of kanji, depending on how long she attended school and how well she kept up with her studies. She would be most likely to read kanji that are in common use for everyday things.

An interesting note here to bring this back to Okada Izo: many of the letters written by Izo's teacher, Takechi Hanpeita, remain in collections and archives. Among his writings are letters to his wife, Tomiko, which he wrote primarily using kana but also occasional kanji within the text. Takechi's letters to Tomiko, I think, are probably a good example of texts that would have been within the reading ability of a lower-ranking samurai's wife.

Maybe this is just me being fandom old and remembering a time on the internet where posting fic didn’t lead to comments unless you somehow programmed them into your website or had a rudimentary message board, which was not easy, but…

I don’t pay attention to numbers, in terms of kudos, hits, and bookmarks. I’ll sometimes check them out—especially bookmarks since it’s fun to see what folks will write.

Before FFN, none of this was really tracked. On FFN you can see statistics on your author dash. MediaMiner provided some info (oh hey that still exists?), but not a lot.

I notice when someone’s going on a reading jag of my work from the daily kudo roundup email. I love that people will comment. But I don’t feel entitled to either, nor am I actively tracking how many comments/kudos I get on each chapter/fic.

I’m not writing fanfiction for that reason.

The algorithm discussion currently going on is interesting to me as someone who used the internet before a) algorithms as we know them today and b) fanfiction archives existed.

There’s been interesting discussion of generational differences with fanfiction expectations, and a lot of discussion of what I might call internet literacy and how it differs when one grew up on algorithms designed to constantly provide content based on your browsing/purchasing habits. Particularly when it comes to social media content (which, let’s face it, is where most people spend their time on the internet).

There’s no denying that this generational gap exists, nor do I think there’s much merit in trying to say one is better than the other.

Rather, I think it’s important to learn among the generations so that we can find a way to bridge expectations, but in a way that enhances the experience for all.

I don’t personally want to have fics recommended to me by an algorithm, because I can see how the algorithm could lead to abuse. (Any other internet olds remember Google Bombing? Oh, internet 1.0.)

But I also don’t think it’s a completely terrible idea to have the option. So long as I can opt out.

The thing is, if you want it, you’re gonna need to figure out how it’s going to work and actually put the work into it, Gen Z fans. This is an innovative idea, creating algorithms that don’t have to do with advertising. (Let’s face it, we live in a capitalist world, and that’s what most of them are used for.)

But no one is gonna do it for you.

The folks who are now fandom olds put together AO3 as an answer to multiple instances of internet censorship by prior archives. They made it with their own browsing preferences in mind, which are different than those of Gen Z.

The code used is public. Learn how to play with it. See what you can innovate.

It’s not gonna be easy, but no one promised you it would be. It wasn’t when my generation did it.

But in a lot of ways, you’re gonna eventually inherit and shift the way fandom works yourselves. Every generation does. Might as well get started.

I wasn’t a part of fandom before the internet, but there are plenty of folks out there who can explain how that worked. But, yeah, algorithms, kudos, hits, and comments didn’t really happen there, either.

literacy in ireland in the mid-19th century

so I was thinking about whether or not Callan (my oc for @moiraimyths' visual novel, Na Daoine Maithe, check it out) would be literate. (or to what extent they would be literate)

Picture of Callan (commissioned from the amazingly talented @shrimp1y tysm again ily!!)

Since Cal is made up, i could simply make up the answer.

However I am instead doing some research on literacy rates in Ireland around 1845, and thought I'd share since this is a bit too much info for just dumping in friends' DMs lol.

Here's the TLDR:

MC lives in Galway, which had very low rates of literacy in 1841, especially for women. However, if you want your MC to be able to read (or read and write) prior to the events of the game, some people were indeed literate.

another point to note is that there's differences in literacy levels. think of someone being able to work their way through a simple text slowly and with difficulty, vs someone who can easily read a complicated text. So your MC might not be fully literate but that doesn't necessarily mean they have absolutely 0 skills with written language. It is up to you!

finally, i just did this research for my own interest. in game, there is an option to read Shae's map, so MC is assumed to be literate. Please don't let any of this information take away from your fun. Ignore it entirely if you don't like the concept of your MC being illiterate! Historical accuracy isn't the point - having fun is.

detailed info and some statistics below the cut!

Here's a county map of Ireland. You can see Galway is on the West coast, at the bottom of the Connaught region (blue).

The Éireannach (your MC!) is travelling to Galway in the beginning of the prologue. Galway is referred to as the closest city, and must be close by for it to have been reasonable for MC to be travelling there. The city is on the coast, in the bay of Galway.

In one of the asks from the devs, it was mentioned that they see the LIs speaking primarily in a Connaught accent as well.

Therefore, I'll be using Galway-specific information if it is available. Failing that, information for Connaught, or the West coast, and then lastly information for all-Ireland.

Now for some more specific details!

General information:

roughly 50% of the irish population was able to read in 1850

30% for men and 45% of women getting married in 1850 in ireland were unable to sign their own name in the marriage registrar

most schools charged for learning to read and learning to write separately, and also sequentially. therefore, more people learned how to read and then stopped paying before they learned how to write, which is why the numbers differ.

also in the northeast of ireland (more industrialised in the mid-19th c) there was a higher rate of literacy, whereas most people on the west of ireland were illiterate

it was also easier to practice reading than writing, so the skill of writing would decay faster too

there was surprisingly detailed information taken in the Irish censuses between 1840-1860 (more detailed than that of the British ones, which were administered separately) so that is convenient for me personally.

Gender differences

Across Ireland, the majority of literate women could read but could not write.

However, boys tended to be kept in school longer than girls. The majority of literate men were able to both read and write

Interestingly, in Galway (the county and the city), most literate women could both read and write.

HOWEVER, the majority of women in Galway were illiterate, and could neither read nor write.

In 1861, about 37% of the female population of Connaught was literate.

darker = more literate, and lighter = less literate.

The darker counties (in Ulster) had a significantly higher literate female population, however most of them could read only, and not write as well.

There also wouldn't have been much reading material available for the general public - "there was only a limited supply, largely religious and produced in the towns of Cork, Limerick, Waterford and south Tipperary" (these are all located in the south of Ireland, i.e., pretty far away from where our Éireannach lives).

Most of this information was taken from a paper called 'Varieties of literacy in nineteenth-century Ireland: gender, religion and language' by Niall Ó Ciosáin. I found this online and downloaded for free.

Literacy in the United States was categorized by the National Center for Education Statistics into different literacy levels, with 92% of American adults having at least "Level 1" literacy in 2019. Nationally, over 20% of adult Americans have a literacy proficiency at or below Level 1. Adults in this range have difficulty using or understanding print materials. Those on the higher end of this category can perform simple tasks based on the information they read, but adults below Level 1 may only understand very basic vocabulary or be functionally illiterate. According to a 2020 report by the U.S. Department of Education, 54% of adults in the United States have English prose literacy below the 6th-grade level.

In many nations, the ability to read a simple sentence suffices as literacy, and was the previous standard for the U.S. The definition of literacy has changed greatly; the term is presently defined as the ability to use printed and written information to function in society, to achieve one's goals, and to develop one's knowledge and potential

The United States Department of Education assesses literacy in the general population through its National Assessment of Adult Literacy (NAAL). The NAAL survey defines three types of literacy

Prose: the knowledge and skills needed to search, comprehend, and use continuous texts. Examples include editorials, news stories, brochures, and instructional materials.

Document: the knowledge and skills needed to search, comprehend, and use non-continuous texts in various formats. Examples include job applications, payroll forms, transportation schedules, maps, tables, and drug and food labels.

Quantitative: the knowledge and skills required to identify and perform computations, either alone or sequentially, using numbers embedded in printed materials. Examples include balancing a checkbook, figuring out tips, completing an order form, or determining an amount.

Modern jobs often demand a high literacy level, and its lack in adults and adolescents has been studied extensively.

how do i learn to think for myself and do my own research before forming my own opinions on issues? how do i find unbiased sources? i feel like i'm too easily influenced my own good. like if a blog i like say "bloobo beeping is bad and everyone who ever blobbed is a beep" i will start to think its true even though i dont know if it is or doubt it. especially if the person i admire and trust their opinion.

This is a great question! We often talk about how media literacy is really important, but we don't always talk about how to actually evaluate the information that we get.

Finding an Unbiased Source

The first thing I would suggest is checking out the Ad Fontes Media Bias Chart. This is a resource that ranks sources based on their left-right bias and how factual their reporting is. The sources that are at the top towards the middle and middle-left are generally trustworthy. The ones below the green line should be treated with caution, and may be trying to convince you to take a particular point of view. If you see a person talking about an issue on social media, it's best to assume that they're towards the bottom of the chart unless you have reason to believe otherwise (they're a journalist associated with a respectable news outlet, they're an expert in their field, etc).

Logical Fallacies

Once we know what sources are generally reliable, it’s important to understand how data can be distorted. This is a good introduction to some of the common logical fallacies that are used when trying to convince people of a point. Logical fallacies are common types of mistakes that people make when putting together an argument, and they're important to look out for when you're being introduced to a new idea. Logical fallacies are especially common on social media sites like Reddit and Tumblr, but they also appear in more “official” media like news articles and online publications. These include arguments like the Slippery Slope argument (“If we ban Hummers because they are bad for the environment eventually the government will ban all cars, so we should not ban Hummers.”), hasty generalizations (”even though it’s only the first day, I can tell this is going to be a boring course.”), begging the claim (”Filthy and polluting coal should be banned.”), and ad hominem attacks (”Green Peace’s strategies aren’t effective because they are all dirty, lazy hippies.”) among others. If you see a person using a logical fallacy to support their argument, it might be worth asking why they're resorting to that kind of tactic instead of letting the facts speak for themselves.

Lies, Damn Lies, and Statistics: Data Manipulation

It's also really important to understand how data and statistics can be manipulated in order to support a given claim.  We see this a lot in the news lately- both sides of a debate appear to have equally valid "facts" that totally contradict one another. This is usually because one side (or both sides) are manipulating the statistics to make them look like they support the claim that's being made, even when they really don't.

For example, people will use “relative risks” to make a problem look bigger than it actually is.  Saying that murders have doubled sounds like a huge problem, but it’s not really that big of an issue if the number of murders went from 1 to 2.  Similarly, people will use “only x number of people” or “over x number of people” to make a problem sound bigger or smaller, even if the reality is that it’s the same number of people.  This powerpoint from UCSD does a good job of illustrating some of the more common ways that data is manipulated to prove a point. 

Graphs and other visuals can also be manipulated to make a problem seem bigger or smaller than it actually is.  For example, this graph was shown in Congress to convince people to defund Planned Parenthood in the US:

The red arrow shows abortions increasing over time and cancer screenings and other preventative services decreasing over time.  This isn’t an outright lie, but the graph above is actually two graphs overlaid and hasn’t been corrected for scale.  When you correct for scale, it looks more like this:

Which is a way different takeaway than the first graph had. 

When talking about statistics, it’s also important to remember that correlation does not equal causation.  This is a logical fallacy like the ones we were talking about before. If you want to be fancy, it's called "post hoc ergo propter hoc". It just means that someone is assuming that if ‘A’ happened after 'B’ then 'B’ must have caused 'A.' 

For example, here’s a graph of people who died by falling out of their beds compared against the number of lawyers in Puerto Rico.  At first glance, it might seem like these two are related in some way, since the graphs line up (they’re correlated).  But obviously, people dying by falling out of their beds has no impact on the number of lawyers in Puerto Rico or vice versa.  There’s no causal relationship between the two of them.

Once you understand the ways that media can be manipulated, it becomes easier to think critically about what you’re reading, hearing, or seeing. 

Some Questions To Ask Yourself

When you interact with something new, there are a few questions you should ask yourself in addition to keeping your eyes open for logical fallacies and data manipulation. 

Who is the author? What is their bias?

What is the publication? What is its bias?

What is the author's purpose for writing this article?

What is the author's point of view on this issue?

What is the author trying to convince me of?

What assumptions is the author making in this article? Try to challenge these claims in your own mind.

Be wary of articles that are using emotional words or manipulation in their headlines ("politician SLAMS innocent young girl for suggesting that families are important" or "Perverted teacher indoctrinates kindergarteners into CRT cult!"). Also be wary of articles that ask a question in the headline ("are teachers indoctrinating our children?" or "will playing video games give you cancer?"). The answer to any headline question is pretty much always "no".

If you can, look for other sources that corroborate what you’re reading (especially if it’s a social media post) and sources that challenge what you’re reading.  Getting a full picture of the issue is important in being able to critically interact with media even if you already know which side you think you agree with.  Try to consider alternative explanations for the issue and the larger context of what you’re reading about to prevent just taking the article at face value.

Lastly, it’s important to understand your own biases and how they might impact what you believe when interacting with new information.  Humans are prone to confirmation bias (interpreting new information as confirmation of our existing beliefs, or throwing out information that doesn’t mesh with our existing beliefs), and so it’s important to understand what your biases are in order to critically interact with new information.  The goal is to be open to new information but aware of all of the extraneous factors that complicate our relationship to media.  Many people see changing their mind as a weakness, but I see it as the ultimate strength.  How great is it that we can grow and change the more we learn?  How great is it that every day, we have the opportunity to be just a bit better than we were yesterday?  Being able to admit that we were wrong is important for emotional growth, and I wish more people were willing to do it.

Your hashtags make it seem like you’re very passionate about your recent reblog about one story having a trope being fine versus the entire trope being bad.. I’d like to hear more about your opinion behind it if you wouldn’t mind sharing!

thanks anon! i suppose all my exclamation points were a bit of a give away. i should clarify perhaps that what i am passionate is less that specific topic than the general notion of statistics.

(this got real long and a bit meandering, but i will not apologize, because i think its fascinating)

we as humans are...really bad at statistics. honestly, thats being kind. we are terrible at understanding statistics. you see a weather forecast which says 20% chance of rain, and while you know, logically, that means there is some chance of rain, your brain basically reinterprets this to: It Will Not Rain. 20% is the same as zero percent. if it rains, then the probability was actually 100%, and the forecast was wrong. but actually, it was 20%, and some random winds blew just right and your campfire got drizzled on.

statistical literacy is wildly important for understanding large societal trends and also wildly undertaught. (i am particularly salty that a few years after i graduated my province removed statistics from the high school math curriculum. i love math, i really do, but while i think theres value in people knowing about calculus and the ways we can use it, most people wont have to actually use it themselves. everyone can benefit from understanding how to read statistics better. in our current society, its almost a critical survival skill)

(i also want to add a caveat which is that while i love statistics, statistics are also incredibly easy to lie with. no, seriously. this is part of the statistical literacy i am talking about. if you like reading academic articles, i recommend this one, which discusses how if you analyze the data differently, you can often get large variations in results. if the pdf is not free for download, the unpaywall extension will grab it for you)

(relatedly, this is why i am of the opinion that you should be careful of any statistic that is presented in only one way. This Medication Doubles Your Risk of Blood Clot! (the risk went from 1 in ten million to 2 in ten million))

so, statistics are important. statistics reveal a lot about society. but we dont experience life as statistics, we experience life as a series of events (or anecdotes). large numbers are not something that we actually evolved to understand very well. we process the world through stories and examples, and we have to learn to think carefully about numbers.

(even watch: i am about to use an anecdote to centre this discussion. i use it to place my point somewhere, because thinking about it in abstract is difficult to do without practice. but it is also falling prey to exactly the fallacy that i am talking about)

i was discussing last summer an incident in which a Black athlete tested positive for marijuana and was given a temporary suspension. she claimed this was a racist ban, and a bunch of discourse ensued. (please note: i am not interested in discussing the validity of her specific claim, which is why im not linking to a specific incident. this could apply to any number of similar examples, and others have likely done more in depth analyses of the particular event. i am using it as a rhetorical device to discuss bias and how it manifests, and am not interested in debating about this one incident. pretend it is a fictional example, if you wish.)

with all those caveats out of the way, here is why this is a difficult claim to make: in isolation, there is very little substance to it. there was a rule, which a person broke, and they received the standard consequence for it. its hard to argue bias when the narrative is so linear. especially if we assume the situation has no deeper narrative (as we know, often stories like this contain more blatant bigotry, while only the softest version makes the headline.) there is little to support the idea that this was a racist decision. moderates might see this and, without more context, think that its a lot of fuss kicked up about nothing. because on its own, it is.

what makes this a racist incident isnt (necessarily) that it was a specific Black athlete that was hit with the suspension. its that there is a trend wherein Black athletes are more likely to be tested, and more likely to be punished rather than dealt a warning.

which returns me to my point, which is the statistics. literally nobody involved in this specific suspension had to be individually racist, or have racist motivations. hell, this individual suspension could be totally ideologically squeaky clean of racism. but on average, in the system, certain people are being hit harder than others, and that indicates racism in the system. but its hard to tease out that racism because every individual instance of it seems kinda wishy-washy. the trend is only obvious in aggregate, and we are, as ive said, very bad at understanding the abstract statistic, especially when we can look at an individual event and label it (we think) clearly.

we focus on the individual rather than the aggregate, which is a natural, and very human response. rather than address the racism in the system, the discussion becomes primarily about whether the people enforcing the ban are racist individuals.

(i emphasize primarily because this is still a good question to ask; you want to make sure there arent any explicit racists in the system! but it should not be the only question you ask, because there are systemic factors that could produce this bias without a single outright racist person involved)

a lot of systemic bias works under this kind of...plausible deniability schema. this athlete broke a known rule and faced consequences. how is that racism? that femme gay man keeps getting rejected from jobs, but there are lots of other qualified candidates, and its not like resumes can really be objectively classified as 'better' or 'worse.' some people will rank resumes differently. maybe the other candidates were just more qualified!

but when there is an average of femme gay men having trouble breaking into a profession, or of trans women struggling to find housing, or or or or...then it reveals something about the structure of the society that is happening in. the individual incident may or or may not be a manifestation of bias, but the totality is. it is even possible the hiring manager who has rejected all these applications is, themself, unaware that they are subconsciously judging these men as being less qualified based on their voice and bearing.

ie. it is possible to perpetuate bias without any conscious malice. and it is very frustrating to our brains, which like stories, when we cant find a clear villain and hero. when its just an evil hiring manager, the situation is comprehensible. when its a bunch of neutral parties that all have the same cultural framework influencing their decisions subtly in a negative direction, its harder to process. and also a lot harder to fix. being able to point to an Evil Hiring Manager is nice because we can boot that person, and the problem is fixed. it feels good to be able to fix a problem, and systemic factors are way harder to actually address.

which is also why its sometimes very hard to criticize things like media trends (yeah, we're looping back to that original post now, im getting there) because we are so much better at processing individual stories than statistics. the problem is often not a specific narrative--ie. a gay character dying in a story--but an overarching trend--ie. all gay characters in stories die. pick one single story out of this trend, and you learn nothing. it may be that in isolation, this story is moving and beautiful, that the death was narratively necessary, that it spoke to a lot of people. individually, the story could be a net good. and so when the trend is criticized, those who enjoyed the individual story, rather than have a more nuanced discussion about what the trend reveals about how our society treats queer people, and how we subconsciously think about queer narratives, people jump to defend the one story in isolation. the point of criticizing a trend is not (or should not be, or generally should not be) to say: This Story Can Never Be Told. it should be to ask: why is this the only story being told?

the question when looking at a trend, or a statistic, should be: what does this imply about what is going on under the surface. and well meaning people who are anti-bigotry can fall prey to whats under the surface. hell, people who are members of the groups in question can fall prey to whats under the surface. i'll give a fandom example (since i do fandom shit on this blog).

now most people who are in fandom would probably agree that trying to label one member of a queer couple the 'man' and one member the 'woman' is stupid. theyre both the same gender! thats the point!

but when you think of your favourite blorbos...you know, right? even if you vehemently disagree, even if you think that way of looking at relationships is stupid...you know. you know which one is on average treated within the fandom as taking each role. even by people who are vocal allies, or who are queer themselves. maybe your instinct is to push this knowledge away from you, to claim you only know it because other people are biased, but you do know it. you are aware of the general trend. we cant help it. we are part of this society, so we know about it, even if we dislike it. and even if we dislike it, we can still accidentally absorb it.

heres a metric i find interesting: when constructing an au of a queer couple based on a het story, which member of the queer couple gets the womans role, and which gets the mans?

even the most progressive het stories still, on some level, have absorbed the cultural context we live in. most stories, not being the Most progressive, will be saturated with many, many small gender stereotypes. it will influence what careers the characters have, what type of choices they make in the narrative, how they interact with the other characters, etc. its not always blatant, but its often an undercurrent (and sometimes it really is blatant).

one would expect, then, that with a queer couple, sometimes it will make sense to mold the het story one way, sometimes the other. but if i look at my own fics that are based on het couples...both of them i have the same character taking the mans role and the other the womans. and i know that falls in line with who fandom as an average treats as the 'woman' in the relationship and the 'man'.

now, two fics does not a significant dataset make, but i also have no evidence that had i not started to think about this trend, i would not have continued to accidentally follow it. and i would wager if you did a survey of all fics in a fandom, you would not find a vaguely 50/50 split, with half the time one character getting one role and half the other, but some kind of skew in the expected direction.

what does this mean? i would hope, in reading either of the fics ive just referenced, few people would walk out saying 'that was homophobic.' the stories themselves are almost irrelevant to the discussion. but it does serve as a probe. somewhere, in the bottom of my mind, whether i want it there or not, gender and sexuality stereotypes are still fermenting quietly. growing like long-lived weeds in places they were planted when i was so small i dont even remember it. tiny seeds planted daily by subtle interactions with the world. being aware of this makes me a little more able to prune back some of these weeds. to interrogate my own beliefs and counter them. and it came not out of a specific homophobic statement or act that i made, but a statistical trend that would be too subtle to notice in isolation. if i look at each story on its own, i can offer very good explanations for why i made the choices i did. but somehow i still managed to blindly find my way into a stereotypical cave.

i use myself as an example because i dont want to point fingers and say: These People are being bad. its not a valuable way to approach narrative analysis. if you are looking at broad media trends, the goal is to understand what societal beliefs and motivations might lead to that trend, even if that wasnt the specific motivation of an individual creator who happens to fall under the trend. every single creator may, individually, have a very good reason for the choices they made. but the fact that we all made the same choice tells us something. and maybe what it tells us is its time to explore some alternative choices.

no individual story needs to be at fault. but it is very hard to separate the statistics from the individual, because our brains are literally programmed to focus on the personal. a story does not have to be bad to be part of a trend. pieces of a trend can be good even if the trend itself is bad.

which is a very long winded way of saying that bias is statistical, and people are bad at statistics. when apes started to walk around and start talking, there was no evolutionary pressure into understanding exactly what a percentage means. it takes conscious effort to not take statistics personally. your favourite story doesnt need to be monstrous to spend time thinking about why a popular trope it happens to contain might be rooted in something you dont believe in.

i will leave you with one final bit of wisdom when it comes to statistics: be skeptical of any statement containing absolutes.

yes, even this one

UNTRUE BELIEF ABOUT READING INSTRUCTION தமிழ்

Learning to read is a natural process. It has long been argued that learning to read, like learning to understand spoken language, is a natural phenomenon. It has often been suggested that children will learn to read if they are simply immersed in a literacy-rich environment and allowed to develop literacy skills in their own way.

This pernicious belief that learning to read is a natural process resulting from rich text experiences is surprisingly prevalent in

education—despite the fact that learning to read is not only unnatural, it is one of the most unnatural things humans do.

There is a difference between learning to read text and learning to understand a spoken language.

Learning to understand speech is indeed a natural process; starting before birth, children tune in to spoken language in their environment, and as soon as they are able, they begin to incorporate a language.

If the linguistic environment is not sufficiently rich or if it is confusing, the innate drive to find a language is so strong that, if necessary, children will create a language of their own (examples of this include twin languages and pidgin languages).

Given the opportunity, children will naturally develop all of the essential comprehension skills for the language to which they are exposed to with little structured or formal guidance.

By contrast, reading acquisition is not natural. While the ability to understand speech evolved over many, many thousands of years, reading and writing are human inventions that have been around for merely a few thousand years. It has been only within the past few generations that some cultures have made any serious attempt to make literacy universal among their citizens.

If reading were natural, everybody would be doing it, and we would not have to worry about

dealing with a “literacy gap.

”According to the National Institute for Literacy and the Center for Education Statistics USA, more than 40 million adults in that country alone are functionally illiterate, and despite their best educational efforts, approximately 40 percent of their fourth graders lack even the most basic reading skills.

These staggering numbers provide evidence that reading is a skill that is quite unnatural and difficult to learn.

வாசிப்பு வழிமுறை பற்றிய தவறான நம்பிக்கை

படிக்கக் கற்றுக்கொள்வது இயற்கையான செயல். பேசும் மொழியைப் புரிந்துகொள்ளக் கற்றுக்கொள்வது போல, படிக்கக் கற்றுக்கொள்வது இயற்கையான நிகழ்வு என்று நீண்ட காலமாக வாதிடப்படுகிறது. கல்வியறிவு நிறைந்த சூழலில் வெறுமனே மூழ்கி, தங்கள் சொந்த வழியில் எழுத்தறிவு திறன்களை வளர்த்துக் கொள்ள அனுமதித்தால், குழந்தைகள் படிக்கக் கற்றுக்கொள்வார்கள் என்று அடிக்கடி பரிந்துரைக்கப்படுகிறது.

செழுமையான உரை அனுபவங்களின் விளைவாக படிக்கக் கற்றுக்கொள்வது இயற்கையான செயல் என்ற இந்த கேடுகெட்ட நம்பிக்கை வியக்கத்தக்க வகையில் பரவலாக உள்ளது.

கல்வி - படிக்கக் கற்றுக்கொள்வது இயற்கைக்கு மாறானது மட்டுமல்ல, மனிதர்கள் செய்யும் இயற்கைக்கு மாறான செயல்களில் ஒன்றாகும்.

உரையைப் படிக்கக் கற்றுக்கொள்வதற்கும் பேசும் மொழியைப் புரிந்துகொள்வதற்கும் வித்தியாசம் உள்ளது.

பேச்சைப் புரிந்து கொள்ளக் கற்றுக்கொள்வது உண்மையில் இயற்கையான செயல்; பிறப்பதற்கு முன் தொடங்கி, குழந்தைகள் தங்கள் சூழலில் பேசும் மொழியை இசைக்கிறார்கள், மேலும் அவர்களால் முடிந்தவுடன், அவர்கள் ஒரு மொழியை இணைக்கத் தொடங்குகிறார்கள்.

மொழியியல் சூழல் போதுமான அளவு வளமாக இல்லாவிட்டால் அல்லது அது குழப்பமாக இருந்தால், ஒரு மொழியைக் கண்டுபிடிப்பதற்கான உள்ளார்ந்த உந்துதல் மிகவும் வலுவானது, தேவைப்பட்டால், குழந்தைகள் தங்கள் சொந்த மொழியை உருவாக்குவார்கள் (இதற்கு எடுத்துக்காட்டுகளில் இரட்டை மொழிகள் மற்றும் பிட்ஜின் மொழிகள் அடங்கும்).

வாய்ப்புக் கிடைத்தால், குழந்தைகள் தாங்கள் வெளிப்படும் மொழிக்கான அத்தியாவசியப் புரிந்துகொள்ளும் திறன்கள் அனைத்தையும் சிறிய கட்டமைக்கப்பட்ட அல்லது முறையான வழிகாட்டுதலுடன் இயல்பாகவே வளர்த்துக் கொள்வார்கள்.

மாறாக, வாசிப்பு கையகப்படுத்தல் இயற்கையானது அல்ல. பேச்சைப் புரிந்துகொள்ளும் திறன் பல, பல ஆயிரம் ஆண்டுகளாக உருவாகியிருந்தாலும், வாசிப்பதும் எழுதுவதும் சில ஆயிரம் ஆண்டுகளாக மனிதனின் கண்டுபிடிப்புகள். கடந்த சில தலைமுறைகளுக்குள் தான் சில கலாச்சாரங்கள் தங்கள் குடிமக்களிடையே கல்வியறிவை உலகளாவியதாக மாற்ற தீவிர முயற்சிகளை மேற்கொண்டுள்ளன.

வாசிப்பு இயல்பாக இருந்தால், எல்லோரும் அதைச் செய்வார்கள், எழுத்தறிவு இடைவெளியைக் கையாள்வது பற்றி நாம் கவலைப்பட வேண்டியதில்லை.

"தேசிய எழுத்தறிவு நிறுவனம் மற்றும் கல்வி புள்ளியியல் மையம் USA ஆகியவற்றின் படி, அந்த நாட்டில் மட்டும் 40 மில்லியனுக்கும் அதிகமான பெரியவர்கள் கல்வியறிவற்றவர்களாக உள்ளனர், மேலும் அவர்களின் சிறந்த கல்வி முயற்சிகள் இருந்தபோதிலும், அவர்களின் நான்காம் வகுப்பு மாணவர்களில் தோராயமாக 40 சதவிகிதத்தினர் அடிப்படை வாசிப்புத் திறன்களைக் கூட கொண்டிருக்கவில்லை. .

இந்த திகைப்பூட்டும் எண்கள் வாசிப்பு என்பது இயற்கைக்கு மாறான மற்றும் கற்றுக்கொள்வது கடினம் என்பதற்கான சான்றுகளை வழங்குகிறது.

Essential Skills to Thrive in an Automated Business Landscape

Essential Skills to Thrive in an Automated Business Landscape

Artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and even robotics are changing the landscape of business at a rapid pace. You might have some misgivings about the rise of automation business, but have you ever considered the benefits?

Below, we will examine why automation is a good thing and the skills you can cultivate to get hired and enjoy job security in an automated environment. We will also discuss the skills automation business owners should learn or hire in to boost sales and the customer experience.

Why You Should Not Fear Being Replaced

Computer automation is currently creating a shift in the workplace, leaving some people uneasy. Could an automated assistant replace you in your job?

To answer that question, it is beneficial to look at another time when machines started performing common workplace tasks. From approximately 1760 to 1840, a massive shift known as the Industrial Revolution occurred. Before the Industrial Revolution, most goods were painstakingly made by hand. During this time, machines were invented to take over repetitive and laborious tasks. Many goods were then made in factories.

Craftsmen of the time feared that these factory machines would take away their jobs. The net result, however, was the creation of more jobs. The work involved was different, but jobs were available.

Something similar happened when computers first came into use in the 1970s and 1980s, and it is happening now. AI and automation may make some jobs obsolete, but the technologies also create a host of jobs.

You can benefit from this pivot by adding the following to your skillset.

How Employees Can Thrive Alongside Automation?

More companies are using automation to handle mundane and repetitive tasks. This ultimately frees up time for many employees to focus on more creative or managerial duties—work that a computer can’t do.

But what if you are currently looking for a job? It is important to present yourself as someone having the technical skills and abilities needed to thrive amid automation—to utilize the technologies at hand. Because numbers don’t lie, a resume with metrics can be the tool you need.

Metrics or key performance indicators (KPIs) record quantifiable, numerical evidence of past performance in your experience section. This might include statistics such as growth percentiles, sales increases, waste reduction, savings in time or money, increases in customers, and the like.

Consider a few key skills you can start cultivating today.

Stay Ahead of the Tech Game

Companies embracing automation want employees ready to do the same. Learn how to use AI text and image generators for brainstorming, proofreading, and document optimization. Familiarize yourself with time-saving digital strategies, like creating electronically signable PDF files. Try out simple automations for yourself—experiment with AI-enhanced smartphone apps, smart home appliances, and more.

Actively improve your data literacy. Use AI tools to analyze and gain insights from data. Automate some of your own administrative tasks so that you can focus your attention on higher-value activities.

This familiarity will give you confidence and an edge when your company introduces the next automation innovation into its workflow.

Soft Skills—Your “Human Advantage”

There will always be things that machines cannot do. You can future-proof your career by polishing in-demand soft skills. These include team building, client relationship building, creative problem-solving, and emotional intelligence.

How Businesses Can Thrive Via Automation?

Above, we’ve considered what job seekers and employees need to know in order to ride the wave of automation successfully. But there are also skills and abilities automation businesses need to utilize.

If you’re a business owner, you might focus on automations that enhance customer experiences. You can learn about these technologies yourself or hire candidates with the necessary know-how.

One such automation is the customer service chatbot. Chatbots can be easily installed on your website. They draw on ChatGPT and similar technologies, and can be trained with the specifics of your automation business. Chatbots provide customers with the answers to common questions, alleviating long wait times. They can also get the ball rolling, collecting information human customer service reps may need to provide additional assistance.

In a similar vein, you can set up automation for email messaging and social media. Keep your company front-of-mind by programming automated emails and message responses. Automatic responses can assure customers that their request has been received.

Schedule regular social media posts to improve brand recognition and generate excitement. Facebook, for example, allows you to schedule posts up to 29 days in advance.

Depending on your industry, you may be able to automate some physical tasks. A robotic vacuum could keep your showroom tidy, or a robotic waiter could bring orders to the table. This can save your company the expense of hiring out these mundane duties.

Key Takeaways

Increased automation is nothing to fear. Job candidates and current employees can solidify their positions by enhancing their technological skills—automation business to improve customer service and save expenses regarding simple tasks.

UK Boosts Global Food Security with IFAD

The UK's Commitment to Global Food Security

In a groundbreaking and ambitious move, the UK is significantly increasing its support to the International Fund for Agricultural Development (IFAD). This strategic initiative is not just a response to the growing concerns of food scarcity worldwide; it's a proactive measure to support and empower poor rural farmers. These farmers play a crucial role in feeding a significant portion of the world's population, especially in developing countries.   Investing in Rural Farmers: A Step Towards Ending Hunger Rural small-scale farms are responsible for producing up to 70% of the food consumed in low- and middle-income countries. This statistic underscores the vital importance of supporting these rural communities. With its generous pledge of £66.7 million to IFAD, the UK government is taking a decisive and impactful step towards protecting these essential livelihoods. This investment also addresses the escalating global hunger crisis, which has become a pressing issue in recent times.  

Addressing the Hunger Crisis: A Critical Time for Action

The number of people facing severe food shortages has risen alarmingly by 34% since 2021. This surge makes the UK's support for IFAD more critical than ever. With an increasing number of individuals, especially in regions like Africa, grappling with hunger and malnutrition, reversing this trend has become a top priority for the UK government and the global community at large.   Championing Sustainable Agricultural Practices As a founding member of IFAD, the UK is at the forefront of promoting and funding sustainable agricultural practices. This significant financial contribution will aid IFAD in implementing a variety of projects designed to enhance farming yields. These projects focus on better soil and pest management, efficient fertilizer use, and access to high-quality seeds. Furthermore, these initiatives play a crucial role in empowering rural women by improving their financial literacy, connecting them to markets, and helping them grow more food sustainably.   IFAD's Impact: Aiming High for Rural Support IFAD's ambitious goal to support over 100 million poor rural individuals reflects both the scale of the global challenge and the commitment required to address it. The UK's substantial contribution to IFAD is a clear testament to its dedication to eradicating rural poverty and hunger. This support is expected to have a far-reaching impact, transforming the lives of millions who depend on agriculture for their livelihoods.  

The UK's Vision: Reflecting on Food Security

Andrew Mitchell, Minister for Development and Africa, underscores the urgency of the global food security crisis in his statement. He reminds us that the UK's investment in IFAD goes beyond mere financial support; it represents a commitment to adopting smarter, more sustainable farming practices and land management. These practices are expected to not only increase crop production but also reduce waste. The UK's approach is grounded in the belief that renewable technology can help farmers rise to the climate challenge in a manner that reduces greenhouse gas emissions and protects the natural environment.   IFAD's Gratitude: Strengthening a Vital Partnership Alvaro Lario, President of IFAD, expresses deep gratitude for the UK's generous support, recognizing its pivotal role in transforming the lives of the world's poorest and most vulnerable rural people. The UK's contributions, particularly in the realm of climate adaptation for small-scale agriculture, have been vital in building resilient food systems in the face of climate change.  

Leveraging Funds for Climate Resilience

The UK's leadership in IFAD’s Adaptation for Smallholder Agriculture Programme (ASAP) not only showcases its unwavering commitment to climate-resilient agriculture but also sets a precedent for global cooperation. Importantly, for every dollar invested in ASAP, IFAD has been able to leverage $6.5 from other governments and organizations. Consequently, this funding significantly helps integrate climate work into all IFAD agricultural projects. As a result, an additional 3.2 million people receive support in coping with the impacts of climate change, thereby enhancing the resilience of these communities.   Sources: THX News, Foreign, Commonwealth and Development Office & The Rt Hon Andrew Mitchell MP. Read the full article

Revealing Hidden Facts: Denny Ja Doses False Reality

Introduction    In the rapidly information age as it is today, there needs to be foresight in sorting and understanding the various information we receive. One of the famous figures in this field is Denny JA, a communication expert who is also known as a political observer. Denny JA often dismantled the false reality in the community, and in this article we will reveal some of the hidden facts that Denny has revealed.    Denny JA’s explanation of fake reality    In Denny JA’s view, false reality refers to information that is deliberate or accidentally distributed with the aim of manipulating public perception. This can be done through various media, including mass media, social media, and other digital platforms. In this digital era, the dissemination of inaccurate information or even hoaks can be easily spread and influence public views.    Denny Ja has identified several factors that cause the spread of false reality. One of them is the lack of digital literacy among the people. Many people do not understand how to verify the truth of the information they receive. In addition, another factor is the existence of political or economic interests behind the spread of false reality. Some parties may benefit from the dissemination of inaccurate information or hoaks, so they deliberately spread it.    In dismantling fake reality, Denny Ja often uses data and facts that can be accounted for as the basis of his argument. He also gave a clear and detailed explanation of how false information can affect public perception and lead to unwanted consequences.    The hidden facts dismantled by Denny Ja    1. Distribution of hoaks in general elections    Denny Ja has dismantled several hoaks circulating during general elections in Indonesia. For example, in the 2019 presidential election, he highlighted the distribution of false information about certain candidate pairs. This hoax tries to influence the public’s view and lead opinions against candidate pairs opposed by certain parties.    2. Manipulation of statistical data    Manipulation of statistical data is also a form of fake reality that is often encountered. Denny Ja has revealed a number of cases where statistical data presented inaccurately or turned around to obtain the desired results. An example is the use of economic data that is turned around to show conditions that are better than the reality.    3. Sensational news    In the world of journalism, sensation is often prioritized than truth. Denny Ja has dismantled some sensational reports that are not supported by strong facts. This kind of news can affect the public’s view and create wrong opinions.    4. Effect of Social Media    Social media has a big role in spreading false reality. Denny Ja has revealed how inaccurate information or hoaks can quickly spread through social media and affect public views. He has also given advice on how we can avoid the dissemination of false information on social media.    Conclusion    Denny Ja is one of the figures who has dismantled the false reality in the community. He has identified the factors causing the dissemination of false information and provided a clear and detailed explanation of its impact. Denny Ja has also revealed some hidden facts that have led to the spread of false reality in various fields, including politics and the media. Realizing this, we as society must be more critical in sorting and understanding the information we receive, and play an active role in combating the spread of false reality in this digital era.

Check more: Uncover hidden facts: Denny JA Disassembles False Reality

Artificial Intelligence Overview and Concepts

Artificial Intelligence (AI) is a broad field of computer science dedicated to building intelligent machines capable of performing tasks typically associated with human intelligence. While AI encompasses various approaches, the recent advancements in machine learning and particularly deep learning have brought about a significant transformation in almost every sector of the technology industry.

AI enables machines to replicate, and often surpass, human cognitive abilities. This technological progress is evident in innovations such as autonomous vehicles, as well as the widespread adoption of generative AI tools like ChatGPT and Google's Bard. AI is progressively becoming an integral part of daily life, with companies from diverse industries heavily investing in its development.

Machine Learning Vs Deep Learning

Although the terms “ machine literacy ” and “ deep literacy ” come up constantly in exchanges about AI, they shouldn't be used interchangeably. Deep literacy is a form of machine literacy, and machine literacy is a subfield of artificial intelligence.

Machine Learning

A machine learning algorithm is fed data by a computer and uses statistical ways to help it “ learn ” how to get precipitously better at a task, without inescapably having been specifically programmed for that task. Rather, ML algorithms use literal data as input to prognosticate new affair values. To that end, ML consists of both supervised literacy( where the anticipated affair for the input is known thanks to labelled data sets) and unsupervised literacy( where the anticipated labours are unknown due to the use of unlabeled data sets).

Deep Learning

Deep literacy is a type of machine literacy that runs inputs through a biologically inspired neural network armature. The neural networks contain a number of retired layers through which the data is reused, allowing the machine to go “ deep ” in its literacy, making connections and weighting input for the stylish results.

Artificial Neural Networks( ANNs) are computational models inspired by the mortal brain's neural structure. They correspond to layers of connected bumps( neurons), each processing information and making prognostications. ANNs are used in colourful AI operations, including image recognition, language processing, and more, through processes like training and weight adaptation. They are a core technology in deep literacy, powering numerous advanced machine literacy tasks.

Artificial Intelligence (AI) is a scientific domain focused on the creation of computers and machines capable of emulating human-like reasoning, learning, and decision-making processes. It also encompasses scenarios involving data volumes beyond human analytical capabilities.

AI is a multidisciplinary field that draws from various domains, including computer science, data analysis, statistics, hardware and software engineering, linguistics, neuroscience, philosophy, and psychology.

From a business perspective, AI comprises a collection of technologies, primarily rooted in machine learning and deep learning. These technologies are utilised for diverse purposes such as data analysis, predictive modelling, object recognition, natural language understanding, personalised recommendations, intelligent information retrieval, and more.

Unveiling the Power of Business Intelligence: Transforming Data into Insights

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In the fast-paced and data-driven world of modern business, the power of Business Intelligence (BI) has emerged as a transformative force that transcends industries and organizational scales. Business intelligence goes beyond mere data analysis; it's a strategic approach that enables organizations to harness the full potential of their data, uncover hidden patterns, and make informed decisions that steer success. In this comprehensive exploration, we delve deep into the essence of business intelligence, its process, its impact, and the ecosystem that surrounds it.

The Essence of Business Intelligence: Insights that Shape Strategies

At its heart, business intelligence is about turning raw data into actionable insights. It's the process of gathering, analyzing, and visualizing data to uncover trends, correlations, and insights that guide strategic decision-making. These insights aren't just numbers; they are narratives that tell organizations where they've been, where they are now, and where they can go. Business intelligence provides the lens through which organizations gain a clearer view of their operations, customers, markets, and opportunities.

From Data to Insights: The Intricate Journey

The journey from raw data to meaningful insights is a multi-faceted process that involves several crucial stages:

Data Collection and Integration: The process begins with collecting data from diverse sources within an organization, ranging from internal databases to external APIs. Integrating this data ensures that it's comprehensive, accurate, and ready for analysis.

Data Analysis: This stage involves employing various analytical techniques to mine data for insights. Statistical analysis, data mining, machine learning, and predictive modeling are just a few of the methodologies that business analysts utilize to extract patterns, trends, and correlations.

Insight Generation: The insights derived from data analysis provide answers to critical business questions. These insights might include identifying customer preferences, recognizing emerging market trends, forecasting demand, or optimizing operational processes.

Reporting and Visualization: Translating complex insights into understandable formats is crucial. Business intelligence tools generate reports, dashboards, and visualizations that transform data into easily digestible information. Graphs, charts, heatmaps, and interactive visuals make it effortless for decision-makers to grasp the significance of the insights.

Strategic Decision-Making: Armed with actionable insights, organizations can make informed decisions that drive growth and innovation. Business intelligence ensures that decisions are grounded in data, align with organizational goals, and adapt to the dynamic business landscape.

The Business Intelligence Ecosystem: A Tapestry of Elements

Business intelligence thrives within an ecosystem that encompasses not only technological tools but also foundational principles:

Technology Infrastructure: Advanced BI tools and platforms provide the technical foundation for data integration, analysis, and visualization. Cloud computing has democratized BI, enabling scalability, accessibility, and collaboration.

Data Governance: Ensuring data quality, security, and compliance is paramount. Data governance frameworks establish protocols that maintain the accuracy, consistency, and confidentiality of data.

Data Literacy: In a data-driven culture, employees must be data literate. Training initiatives ensure that individuals understand data interpretation, visualization, and analysis, enabling them to contribute meaningfully to decision-making.

Collaboration: Collaboration tools facilitate the sharing of insights across departments and levels. Effective collaboration enhances collective decision-making and ensures that insights are utilized organization-wide.

The Impact of Business Intelligence: Transforming Organizations

The ripple effect of effective business intelligence is profound and extends to various facets of an organization:

Informed Decision-Making: Organizations gain the ability to make informed choices backed by data-driven insights. This minimizes the reliance on gut feeling and intuition, leading to more accurate and effective decision-making.

Operational Efficiency: By scrutinizing operational processes, BI identifies inefficiencies, bottlenecks, and areas for improvement. Data-driven insights enable organizations to streamline workflows, enhance resource allocation, and reduce wastage.

Customer-Centric Strategies: Business intelligence brings organizations closer to their customers by analyzing their behaviors, preferences, and purchasing patterns. Armed with this knowledge, organizations can create tailored strategies that resonate with their target audience.

Strategic Planning: BI enables organizations to formulate long-term strategies by analyzing market trends, competitive landscapes, and emerging opportunities. Organizations can pivot with agility in response to shifts in the business environment.

Risk Management: BI empowers proactive risk management by identifying potential threats and vulnerabilities in real real-time animations can implement contingency plans and mitigate risks before they escalate.

Conclusion: Empowering Data-Driven Excellence

Business intelligence isn't just a tool; it's a mindset that permeates organizations, driving them toward excellence in the digital age. The integration of business intelligence empowers organizations to tap into the wealth of data they generate, transforming it into actionable insights that guide strategies, steer operations, and fuel innovation. The BI journey is a cycle of continual improvement, where insights drive decisions that, in turn, generate more data for analysis.

As organizations continue to evolve, business intelligence stands as an indispensable companion that illuminates the path forward. It empowers decision-makers with the knowledge to navigate complexity, capitalize on opportunities, and adapt to change. In a world where data is the currency of success, business intelligence serves as a beacon, guiding organizations toward data-driven excellence and ensuring that every decision is illuminated by insights that shape success. https://guidancepoint.in/

Is data engineering a promising career for India's technical background students?

The demand for big data professionals has noway advanced. Numerous people are erecting high-payment careers working with big data. Data wisdom is heavily calculation-acquainted. By discrepancy, data masterminds work primarily on the tech side, erecting data channels. The two places have in common that both work with big data. Working with big data frequently takes a big platoon. Data masterminds work with people in roles like data storehouse masterminds, data platform masterminds, data structure masterminds, analytics masterminds, data masterminds, and DevOps masterminds.

Data engineers bridge the gap between software engineering and data wisdom and transfigure data into a useful format for analysis. Let us take an illustration; a data scientist wants to dissect Uber and one of its challengers by checking a user action history compared to the competition and see what conduct relates to druggies who spend further. We should combine information from the garçon access logs and the app event logs to enable them to produce this. Both Uber and its competition are generating huge quantities of data from their mobile app( from riders and motorists).

A data mastermind will produce a channel that will read the mobile app and garçon logs in real-time, parse them, and attaches them to a specific user. It should be a bus-spanning one so that it can accommodate a large number of users.

Steps to Become a Data Engineer

Becoming a Data engineer requires education, practical experience, and nonstop literacy. Following are the ways to pursue a career as a Data engineer.

Step 1:- Earn a Graduation Degree

After clearing class 12, candidates should look to enroll in a UG program fastening on data wisdom and engineering generalities. Some majors that campaigners can pursue include Data Science, Computer Science, Mathematics, Statistics, and Information Technology.

Step 2:- Develop Data Engineering Skills

Data engineering is constantly evolving. The candidates should learn skills to keep up with the trend. Data masterminds should retain a set of specialized and soft chops to exceed in their places.

Step 3:- Gain Hands-on Experience

Candidates should apply for externships to work on different systems and develop the skills needed to be a Data engineer. Internships also help in generating a good portfolio.

Step 4:- Develop a Data Engineering Portfolio

A portfolio is important to demonstrate fresher skills and an understanding of generalities to implicit employers. Candidates should start working on their portfolios by participating in freelance work and structuring sample systems.

Step 5:- Launch application for Data Engineer jobs

After completing their education and gaining significant skills, candidates should choose Data engineer positions and offer services. Candidates can also conclude postgraduate courses for better career openings.

Facts about a Career in Data Engineering

A Strong Developer

It is pivotal for a data engineer to have a strong programming background. They also need a love of or at least an interest in data, in changing patterns in data, or else they may find the work boring. Also, they like and can produce systems that are delicate and complex. Big data systems are more complex than small data. 

Technology Knowledge

Most universities are tutoring programming from an academic point of view, and there is a difference between what the assiduity wants and what academia is furnishing. A university may have classes on programming, but people who want to become data engineers may have to learn the specialized and systems side on their own. A good data engineer values the right tool for the job. Data engineers need to know 10 to 30 different technologies to choose the right tool for the job in technologies. Data masterminds will inescapably need to collude out their channel infrastructures in a clear and presentable way.

Experience versus Education

Anyone with a software background having experience in operations or systems can make a smooth transition to data engineering. Training in software development and data science skills, statistics, and calculation are important. Data negotiating teams generally dispose toward seniors. More astronomically grounded software engineering brigades will have people with a wider range of experience.

Social and Communication Skills

Data quality is extremely crucial when building channels. All downstream work is only as good as the quality and integrity of the data you are moving through the channel. A good data engineer should appreciate the fineness of clean and simple designs that are not over-architected. A good data engineer should find satisfaction in helping their guests break through painful problems.

Typical Earnings of a Data Mastermind

Data engineering is supposed to be the best-paying job in tech and will grow exponentially in the coming times as data explodes and demand for professed professionals increases.

Dices’ 2020 Tech report listed data engineering as the swift-growing job of 2019, growing by 50 times over time. This trend is only going overhead in the coming decades.

According to Glassdoor, the estimated total pay for a data engineer is $118,015 per time in the United States, with an average payment of $97,820 per time. But this is only a median estimate, of course. Our earnings could be advanced or lower depending on numerous factors, including position, cost of living, etc. 

Conclusion

The data structure is necessary for any company looking for data mining ways and gain practicable perceptivity. Numerous of the new data engineers in the industry came from a background in software engineering and brought to this field their skills in Linux, Java, SQL, Python, and Hadoop. As this career continues to grow and change, data engineers can gain influence by staying in the van of advances in data operation.

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