Why do adults vary in how categorically they rate the accuracy of children’s speech?
Bibliographic record
Abstract
In a recent experiment using continuous visual analog scales (VAS) to examine adults’ perception of children’s speech accuracy, listeners varied in the extent to which they categorically perceived children’s English and non-English productions [Munson et al., American Speech-Language-Hearing Association (2008)]. Some listeners utilized all points on the VAS line for their responses, while others grouped responses around discrete locations on the line. It is hypothesized that differences in categoricity of responses across listeners might relate to listeners attending to either categorical linguistic information (i.e., identifying phonemes in a word, which would promote more categorical labeling) or gradient indexical information (i.e., identifying the child’s sex or age, which would promote more continuous labeling). If this is true, it should be possible to elicit differences in categoricity of fricative “goodness” judgments in individual listeners by priming them to listen to linguistic variables (by interleaving fricative judgment trials with trials in which they categorize the vowel spoken by the child) or indexical variables (by interleaving fricative judgment trials with trials in which they identify the child’s sex). This paper reports on an experiment designed to test this. Results will help us better understand individual response patterns in cross-language speech perception experiments.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".