The Sociolinguistic Distribution of and Attitudes Toward Focuser <i>like</i> and Quotative <i>like</i>
Bibliographic record
Abstract
This paper accomplishes three tasks: it considers the actual age and gender distribution of like in a corpus of informal U.S. English, compares the findings of that study with the perceived age and gender distribution as determined by a questionnaire study and a matched‐guise study, and analyzes specific sociolinguistic stereotypes associated with this usage. It is found that younger people use both kinds of like more often than older people do, and that men and women use it approximately equally often. The perceived age and gender distribution is quite different, however; young women are perceived as using like most often. Additionally, informants guess the age of like guises as younger than they do the age of non‐ like guises in a matched‐guise study, and also rate like guises more positively in terms of solidarity‐based criteria, but less positively in terms of status‐based criteria.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".