Is <i>like</i> like <i>like</i>?: Evaluating the same variant across multiple variables
Bibliographic record
Abstract
Abstract We explore the linguistic variant – as opposed to the variable – as the object of sociolinguistic perception. The sociolinguistic principle of accountability, in centering the variable as a choice between variants, implicitly models variation from the perspective of the speaker, who makes that choice. But is it a good model for the perception of sociolinguistic meaning on the part of the listener? This paper reports the results of a matched-guise study designed to test eight distinct functions of the form like – as a verb, approximate adverb, discourse particle, etc. – in an attempt to determine whether listeners attach the same social meaning to this variant regardless of the variable it represents, and if so, whether this could be a partial explanation for why the use of like is increasing in apparent time in several variables at once. We do not find evidence to support the hypothesis that listeners evaluate the different functions of like in the same way. However, our results do offer empirical support for the division of like into sociolinguistically salient “vernacular” and non-salient “grammatical” categories. Moreover, we find a consistent pattern in which the more recently a function of like entered the speech community, the more salient it is to listeners.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".