Bibliographic record
Abstract
The present article investigates the other-accent effect (OAE) on speaker recognition in the context of voice line-ups for speakers of Quebecois and Hexagonal (France) French. The literature largely attests to a language familiarity effect (LFE) that can bias the results of this forensic phonetics technique. A far less substantial number of studies have investigated whether this finding also extends to varieties of a single language (regional or social). The main aims of the present study are therefore to test whether such an effect is present for the two varieties of French concerned, and whether the predominance of the so-called “standard” variant of French generates a measurable asymmetry in this effect. Participants (n = 34) whose native French was either Quebecois or Hexagonal took part in a speaker recognition task through two voice line-ups, one for each variety of French. The findings indicate that there is no significant OAE on speaker recognition for the French varieties studied, despite some noteworthy tendencies related to the asymmetry between the two varieties of French and the duration of stay of the French participants in Quebec.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".