Logistic regression modelling for first and second language perception data
Bibliographic record
Abstract
Logistic regression analysis has, for some time, been successfully applied to L1 speech perception data, but has not been widely applied in L2 speech perception research. This chapter is a tutorial which makes use of simple data sets to introduce logistic regression analysis as applied to categorical response data from L1 and L2 speech perception experiments. Data are taken from an experiment on L1 Spanish vowel perception by Álvarez González, and experiments on L1 and L2 English vowel perception by Escudero & Boersma, and Morrison. Model fitting is demonstrated as a technique to determine which acoustic cues are attended to by listeners. Logistic regression coefficients are used to quantify how listeners use those acoustic cues, to produce graphical representations of their use of acoustic cues, and as statistics in secondary analyses used to determine whether there are significant differences in the perception of stimuli by L1 versus L2 groups of listeners.
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How this classification was reachedexpand
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 | 0.014 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.012 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".