Bibliographic record
Abstract
This commentary politicizes the relational-technical economy of biomedicine and the future it forecasts for feminized bodies with chronic illnesses. As digital medical imaging technologies develop, visualizations of disease are becoming more sophisticated. I begin by critically considering the implications this has for feminized bodies with chronic illnesses through the example of endometriosis, a common chronic pain disease that is not well understood within the biomedical paradigm. Enhanced imaging technologies promise to illuminate previously-unknowable aspects of disease pathophysiology, but what future is such technological progress enabling, and for whom? Through a critical intersectional lens, it becomes evident that the biomedical-technological future imag(in)es particular bodies, in particular places, and towards particular, but not unfamiliar ends. Enhancing abilities to visualize disease through digital technologies within a biomedical paradigm does not require us to look differently, which may be precisely what is needed. Thus, drawing theoretically on the work of bell hooks as well as critical feminist disability studies scholarship, I kindle the fire of a critical intersectional politic that transforms biomedical-technological ways of seeing the feminized body with chronic illness. Such a politic not only offers the possibility to imagine alternate futurities, but also contributes to their tangible realization.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".