If I had a magic wand: Speculative everyday anti-racism for addressing workplace racial discrimination in British Columbia's public sector
Bibliographic record
Abstract
Using empirically-derived knowledge from twenty-five interviews with racialized public servants in British Columbia, Canada, this article presents a qualitative exploration of participant-proposed solutions to racial discrimination at work. Participants were selected on the basis of reporting lived experiences with workplace racial discrimination and their insights were collected through in-depth qualitative interviews in response to a speculative question on how they would stomp out the specter of racisms in their workplaces if they had all the power and resources to do so. This study introduces speculative everyday anti-racism as a framework that outlines additional possibilities for resistance, contestation and liberation at work. Speculative everyday anti-racism aims to also offer the discursive and political power to participants and disrupts the practice of experts and academic knowledge-producers providing prescriptions for workplace anti-racist work. The guiding principle behind the design of this study is that if workplaces are to respond effectively to racial discrimination in their midst, racialized workers ought to play a key role in identifying issues and proposing solutions. Thus, it is proposed that a reconceptualization of antidiscrimination grounded on speculative everyday anti-racism could better assist policy makers and practitioners in responding to racial discrimination in the workplace.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.043 | 0.034 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".