Decentering Whiteness in Canadian Bioethics
Bibliographic record
Abstract
Mainstream Canadian Bioethics' overindulgence in 'cutting-edge' issues of medical or technological innovation, coupled with its disregard for critical discussions of race, is indicative of the field's greatest shortcoming.In this thesis, I will explore Whiteness within Bioethics, uncovering its historical underpinnings and the reluctance of the field to confront racial injustices.In doing so, I will reflect on the ways Whiteness has significantly altered the trajectory of mainstream Bioethics, from silencing BIPOC voices to biased allocations of funding, the field has come to reflect values that directly counter the value of justice it's founded on.Drawing on the perspectives of Black and Feminist Bioethics, I will examine the limitations of mainstream Bioethics and the ways that intersectionality and feminist standpoint theory can be implemented to decenter Whiteness.In proposing practical strategies for decentering Whiteness, I will highlight the significance of cross-discipline collaborative approaches and the importance of fostering spaces for anti-racist scholarship.iii Phoebe Friesen, for their unwavering support during my thesis journey.If it wasn't for their patience and support, I would have never been able to make it to this point.Thank you to Dr. Daniel Weinstock for your endless patience, invaluable feedback, and your help in piecing together my disjointed ideas.Thank you as well to Dr. Friesen for your insight into the topic, constructive comments and positive outlook which helped me get through the most difficult parts of my thesis.I am so thankful for the honour of having worked with two incredibly intelligent, compassionate,
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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 | 0.034 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.059 | 0.032 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".