Gender and Racial Expectations in the Workplace: A Comparative Study
Bibliographic record
Abstract
I want to test the generalization that Eurocentric societies have come far, regarding how women and people of colour are treated in a professional setting. Are there more opportunities for marginalized groups and rules in place to combat discrimination? Are people more respectful of different genders and races? In this essay, I will compare two case studies: my own experiences as a young, mixed-race, Indo-Guyanese/French Canadian woman, working part-time jobs from 2018-2020, and those of my aunt, Jenny Rajaballey, who as an Indo-Guyanese immigrant woman (Jenny Rajaballey) had a successful career as a health executive from the 1990s to 2010s. I interviewed her using a questionnaire I composed. My goal was to study how the expectations of women, particularly of color, in the workplace have changed over time and how minimum wage jobs and administrative positions can be compared.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".