Bibliographic record
Abstract
And these are all girls who are pretty smart, and they have some of the little you know idiosyncrasies of youth language, but they're pretty articulate. They can express an idea, provide an opinion and are pretty self-assured and yeah. (Candice Yuranyi, 40) Imagine you overhear a person say: That's like so random and another person say: That's very unpredictable. Which person is almost certainly younger and which older? Which one is using “proper” English and which one is using slang? You might feel you could guess the correct answer to both of those questions. But there's another question that's more interesting: why do young people and old people use language so differently in the first place? Let's consider three basic facts about language: Language is always changing. No one can stop language change – not teachers, not parents, not the prime minister. Age has a huge impact on how a person uses language. In our culture, young people usually try to set themselves apart from the older generation – through clothing and appearance, preferences in music, and acutely through language. That's because language is a very important symbol of social solidarity. The ways we use language let other people know who we are and where we belong. As teens gain independence and come in contact with a wider circle of friends, they are exposed to an increasingly rich range of new language uses. When these new uses spread among more and more teens, new expressions enter English, and sometimes they even influence English grammar. This is how young people become the driving force behind language change. You might also be interested to know that girls are far more likely to use new features of language than boys are, which means that girls are the primary transmitters of new usages. Where do the “new” features of language come from? Young people do not create them out of nothing. As we shall see, young people take the materials already available in the language and modify them in new ways. Here's an example. An older person will almost always use the word very when emphasizing something: “That's very fine.” A middle-aged person will be more likely to use really , as in “That's really nice.” And an adolescent today will undoubtedly say, “That's so cool.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.015 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".