Bibliographic record
Abstract
I'm just thinking 'cause like when I think of stuff as far as like music and culture or like ah like trends and stuff like that, I always think of like I don't know the States and LA and New York and stuff like that. (Brent Kim, 21) I have now investigated a host of words, phrases, and constructions that are typical of teen language at the turn of the twenty-first century. Given the findings and results and observations from each of the chapters, what does it all mean? It should now be apparent that teenagers are not the ones to blame for variation and change in language. Language change is part of language itself. Every generation is different from the last and will be different from the next. Do teenagers use slang? Yes, but so does everyone else, at least some of the time. You have only to notice the many examples from the more elderly individuals in this book. It is even the case that a person will criticize teen language and then use that very same form him- or herself (see Gabrielle Prusskin's quote on page 34). What is slang anyway? It depends on what is included in the group of phenomena called slang at any given point in time. Such words and phrases are typically called “mistakes.” However, language mistakes are not in teenagers; the mistake is in human nature. Who decides what is right and wrong? The only thing that makes a word “lazy,” “sloppy,” or “bad” is how society views it. The value judgment is social and historically time-stamped. What was once slang can as easily become the grammar of the next generation or it can fade into dated oblivion like hwæt , whom , shall , or groovy. When I identified the funky features of teen language at the beginning of the book it may have seemed that they were all the same type of thing – the wacky things that kids say. In fact, they are actually a highly variegated group of linguistic phenomena. Most of them are developments that can be tracked back into much older populations; some have their roots hundreds of years in the past. Some of them are innovative extensions from what has come before. In rare cases words emerge and cover vast distances in time and space in a generation or two.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.045 | 0.037 |
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".