The <i>quiet purge</i>: a qualitative exploration of sidelining, denigrating and dehumanizing racialized public servants in British Columbia
Bibliographic record
Abstract
This article presents a qualitative exploration of racialized public servants’ lived experiences with workplace racial discrimination in British Columbia, Canada. Specifically, introducing the concept of the quiet purge as the theoretical framework that is used to make sense of data, this study examines experiences of participants with racist pressures at work that had the effect of side-lining and pushing them out to the peripheries of publicly funded workplaces. After detailing a variety of strategies used to recruit 25 non-White participants who worked in the public service and took part in this study, findings would be presented as cultural denigration, accent-mediated dehumanization, emotional fatiguing and precarity-breeding work assignment. In analyzing these findings, this article will conceptualize these racist pressures as the quiet purge, or stratifying mechanisms that were designed to peripheralize and marginalize racialized participants in their respective workplaces. Subsequently, the article will briefly discuss my observations of intersectionality and conclude that, considering the fact that some of the experiences that were recounted by participants describe incidents that were glaringly, unambiguously and blatantly racist – struggles that are often seen as the hallmarks of earlier times – workplace racial discrimination in Canada is alive and requires urgent research and policy attention.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.043 | 0.023 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".