Diverse environments and their impact on accentedness judgments
Bibliographic record
Abstract
Research shows that listeners' perceived accentedness can be mediated by visual input (Babel & Russell, 2015; McGowan, 2015; Zheng & Samuel, 2017), and can change depending on their exposure to varied speech (Baese-Berk et al., 2013). Here, we tested the impact that visual input and linguistic diversity has on listeners' perceived accentedness judgments. Two experiments were conducted: one in Gainesville (USA) and one in Montreal (Canada). While these two locations were selected for their bilingual populations, Montreal has a more diverse linguistic landscape (Gullifer & Titone, 2019). Participants completed an accentedness judgment task where they were shown either a White or a South-Asian face while listening to sentences in American, British, and Indian English. They also completed a language background questionnaire, a social network questionnaire, and executive control tasks. In an ongoing study, results show that for Gainesville, participants' perceived accentedness of all three varieties increased when the visual input changed from a White face to a South-Asian face (F(2, 66) = 33.3, p < 0.001). The same effect was not observed for listeners in Montreal (F(2,23) = 0.664, p = 0.524). These preliminary findings suggest that exposure to both different accents and racial/ethnic categories on a regular basis could impact perceived accentedness judgements.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".