Assessing the Specificity and Accuracy of Accent Judgments by Lay Listeners
Why this work is in the frame
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Bibliographic record
Abstract
Historically, there has been less research carried out on earwitness than eyewitness testimony. However, in some cases, earwitness evidence might play an important role in securing a conviction. This paper focuses on accent which is a central characteristic of voices in a forensic linguistic context. The paper focuses on two experiments (Experiment 1, n = 41; Experiment 2, n = 57) carried out with participants from a wide range of various locations around the United Kingdom to rate the accuracy and confidence in recognizing accents from voices from England, Scotland, Wales, Northern Ireland, and Ireland as well as looking at specificity of answers given and how this varies for these regions. Our findings show that accuracy is variable and that participants are more likely to be accurate when using vaguer descriptions (such as “Scottish”) than being more specific. Furthermore, although participants lack the meta-linguistic ability to describe the features of accents, they are able to name particular words and pronunciations which helped them make their decision.
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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 | 0.000 |
| 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