Perceptual identification of talker ethnicity in Vancouver English
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Studies of ethnolinguistic variation typically begin by describing the speech production variables used to index social groups. In this study, we begin with indexical recognition – the perceptual identification of speakers’ self‐identified ethnic groups – to determine whether speakers produce ethnolinguistic variation and whether listeners are sensitive to it. Speech samples were recorded from thirty individuals from Metro Vancouver who self‐identified as Chinese, East Indian, or White Canadian. These utterances were used in a perception task where listeners categorized speakers’ ethnicities. Listeners’ social networks were labeled according to the ethnic group with which they reported spending the most time. Analyses indicate that while speakers vary in their productive expression of ethnolinguistic variation, listeners are consistent in their labeling choices. Listener accuracy was higher for voices from the listeners’ reported social group and White voices. These results suggest that familiarity with ethnic groups through social networks and mainstream culture influences indexical recognition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.045 |
| 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 | 0.000 |
| 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