On the blood of others: re-visiting how “health” happens in interdisciplinary scholarship
Bibliographic record
Abstract
In much interdisciplinary scholarship, when health and well-being are conceptualized, they are often contextualized in white supremacist, racist, and capitalist frames. To reframe scholarship towards more useful possibilities, we first must confront the historical and ongoing violence brought forth by our reliance on notions of health that often come directly from colonization and slavery and have failed to provide possibilities of health for racialized folks. If capitalism can be described as ‘labour on the backs of Others’, then health is founded on the blood of Others – particularly, those who were enslaved. In naming the socio-historical legacies and their current manifestations, we name some of the interconnected oppressions that we must dismantle in our everyday work as we engage with racialized communities. Our work requires us to politicize the role of health and healing as a crucial component of the better world we are building.
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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 | 0.037 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.035 | 0.175 |
| Scholarly communication | 0.036 | 0.036 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".