Variability and reliability in the AXB assessment of phonetic imitation
Bibliographic record
Abstract
Speakers adjust their pronunciation to come to sound more similar to recently heard speech in a phenomenon called phonetic imitation. The extent to which speakers imitate is commonly measured using the AXB perception task, which relies on the judgements of listeners. Despite its popularity, very few studies using the AXB assessment have considered variation or reliability in the listeners’ performance. The current study applies a test-retest methodology focusing on the performance of listeners in the AXB assessment of imitation, which has not been considered explicitly before. Forty listeners completed the same AXB experiment twice, two to three weeks apart. The findings showed that both sessions reach the same overall conclusion: the listeners perceived the same overall amount of imitation in both sessions, which is taken to mean that the shadowers did imitate and that the AXB task is reliable at the group level. Furthermore, the findings show that listeners vary substantially in their performance in the AXB assessment of imitation, but that they are relatively consistent in this performance across sessions. This suggests that differences in AXB performance at least partly reflect differences in ability to perceive imitation, rather than simply random variation.
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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 | 0.012 | 0.044 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".