How the words we teach English language learners changed

I would blame it more on globalization.

In 1953 people were not exposed as much to different groups of people far away.

“It offers fewer words for the people directly around you, but more for belonging at a distance” - the further I get into this article the more it just seems like sampling bias as a narrative.

The author mentions various categories grew or shrank, but always in percentages. Since the list also grew from 2300 to 2800 words that feels like it might distort things a bit: in absolute word count, a category that shrank by 1% lost fewer words than a category that grew by 1% would have gained, no?

Having said that, the categories that shrank all did so by a big enough percentage to also shrink in absolute number of words, so at least that isn't a problem.

> It’s as if the world now requires you to be more precise about everything.

This is something I've been thinking a lot about. We have trended from subjective language to objective language. Why?

Computing. Software is written with objective language. Everything is clearly unambiguously defined. Blue is no longer a category, it's #0000FF. Logic must always reduce to a binary truth value. Most of what we have to talk about is somehow relative to software. Software even structures most of what we write! We don't just talk to each other, we tweet, email, message, post, search, etc. These structures each imply a specific set of phrase structures that can make sense.

Lately, it's hard to go even a day without reading some complaint that such and such was written by "AI" (an LLM). Why is this so obvious? Well, the core advantage that LLMs provide is that they don't compute. Inside an LLM, there is no arithmetic, no logical branches, no truth values. Phrases aren't generated to define or to resolve. They are generated to continue. Sure, we can direct the story to follow the steps of logical deduction, but that isn't anything like calculation. An LLM simply isn't invested in logic, precision, correctness, etc. the way we expect modern writers to be. It's not the em-dashes or the word choice that illustrates this, it's the fundamental perspective of the system.

We are sorely missing subjectivity. Natural language never was, and never will be, computable. You can't reduce a natural story to binary truth values without choosing an arbitrary perspective that resolves its ambiguity. The more precisely abstract our language gets, the more detached from reality our stories become. The more objective our assertions about reality are, the less relevant they can be.

My answer to this is to make the arbitrary choice of perspective a first-class feature. If we can explicitly decide what meaning is relevant, we should be able to weakly solve natural language processing. It seems like a pretty simple and obvious idea, but so far is easier said than done.

I can't quite follow your point. Are you saying there is more inequality today than in 1953?