The medium-term dynamics of accents on reality television: Supplementary Material
Bibliographic record
Abstract
DYNAMIC MODEL RESULTS.Tables S1-S3 supplement the results of the dynamic models reported in §5 of the main text.Table S1 summarizes predicted time dependence in VOT for voiced and voiceless stops for each speaker: predicted average VOT (a speaker's 'offset' in the static model), whether the model includes by-day variability and/or a time trend, and σ, which quantifies the degree of by-day variability.Table S2 summarizes the predicted time dependence in the CSD rate for each speaker (analogously to Table S1): predicted average CSD rate (the intercept of a speaker's dynamic model), whether there is by-day variability and/or a time trend, and σ, which quantifies the degree of by-day variability (when present).Table S3 summarizes predicted time dependence in F1 and F2 for each vowel, for each speaker, similarly to VOT.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.586 | 0.097 |
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".