Intersection of Language, Gender, Race, and Its Impact on Psychological Safety for Black Anglophone Women in the Québec Workplace
Bibliographic record
Abstract
Aim/Purpose: This research aims to assess the extent to which discourse around representation in the Quebec workplace aligns with the experiences of Black Anglophone women. Background: There is a persistent lack of quantitative and qualitative data on Quebec’s English-speaking Black Community (ESBC). With the increase in implementation of diverse, equitable, and inclusive policies in the workplace, and recent French-language laws, it is crucial to collect current and meaningful data on the community’s experiences. Methodology: Data collection methods included interviews, surveys, and content analysis. Interviews were conducted with a small, diverse group of eight (8) Black Anglophone women aged 18-65. Findings: Québec’s language politics creates additional barriers for Black Anglophone women in the workplace, even in a working French proficiency environment. The language social stratification hinders their work quality even after Diversity, Equality, and Inclusion policies have secured them a position. Impact on Society: The volatile language politics in Québec intersect uniquely with gender and race, further shifting the goalpost for Black Anglophone women's full inclusion in the workplace. By fostering and encouraging discourse they can identify aspects of psychological safety, empowering themselves and their communities to navigate work environments more effectively.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".