Sali-CAT: A new method for ranking social salience for multiple variables
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Social salience, the association of a social category with linguistic variables, has been hypothesized to be an important driver of language change. This hypothesis has not been rigorously tested due to the lack of a reliable measure of social salience. In this paper, we present Salience Categorization Test (Sali-CAT), a new approach to measuring the association of word variants with social categories across multiple lexical variables. The approach includes a customized experimental paradigm (three alternative forced choice) and a statistical method to establish the baseline Salience Ratio (Sali-RAT) score for word variants that do not have a bias in usage with respect to the social categories. We demonstrate the approach by testing the association of multiple variables with different generations of speakers in the Gurindji speech community.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it