MétaCan
Menu
Back to cohort
Record W4238976769 · doi:10.32920/ryerson.14646990.v1

Hearing Audible Minorities: Accent, Discrimination, and the Integration of Immigrants into the Canadian Labour Market

2021· preprint· en· W4238976769 on OpenAlexaffabout
Alanna MacDougall

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicLinguistics, Language Diversity, and Identity
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsStress (linguistics)ImmigrationLinguisticsPsychologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Accent is a permanent marker of difference for learners of a second language, and may be a barrier to finding appropriate employment. Research on discrimination and accent reveals a widespread belief in the myth of a standard, ideal accent. This has resulted in individuals stereotyping accented speakers and drawing inappropriate conclusions about their language ability, leading to discrimination in both the workplace and broader society. A small study of Ottawa companies conducted for this paper supports the hypothesis that some employers may rely on accent to determine an applicant's English proficiency. Accent discrimination can be addressed by providing employers with information about accent and appropriate tools for language evaluation, confronting the reality of accent discrimination with ESL students, and by broadening the discourse on discrimination as a whole to recognize that minorities can be audible as well as visible.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.202

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0110.004
Scholarly communication0.0050.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.036
GPT teacher head0.246
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2021
Admission routes2
Has abstractyes

Explore more

Same topicLinguistics, Language Diversity, and IdentityFrench-language works237,207