2023 WINGS #44-23 Accented English Unfairly Stigmatized
Bibliographic record
Abstract
Why people whose first language is not English have different accents; how reactions to that affect immigrants’ lives. Speakers: Rosina Lippi-Green PhD in Linguistics, author of English with an Accent ; Evelyne Ello-Hart, from Côte d’Ivoire, program supervisor for the Africa Women’s Coalition in Portland, Oregon, speaks seven languages, including Italian; woman from Laos who works for Refugee Resettlement Program and speaks Vietnamese, Laotian Thai, variants of Chinese, and English; Dora Reina an accountant from Mexico, living in the US and taking English classes; Tracey Derwing, professor in the TESL [Teaching English as a Second Language] program in Educational Psychology at the University of Alberta, who co-directed the Prairie Metropolis Research Centre of Excellence for Research on Migration and Integration where she researched how to teach native speakers of English to be better listeners.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.659 | 0.026 |
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; both teacher heads agree on what is shown here.
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".