ENACTING THE POLITICS OF CARE WITH CHRONIC AND CRIP TECHNOLOGIES
Bibliographic record
Abstract
Drawing from feminist and queer technoscience studies, this panel examines the constitutive entanglements of bodies, technologies and systems in Type 1 diabetic continuous glucose monitoring and the intimate feeling of their numbers and data visualization; in the selfie politics of mastectomies on Instagram and the platform vernaculars people use to make the grief of breast cancer’s gendered loss representable; in the ways Bay Area Deaf AIDS activists in the 1980s and 1990s remediated their access to information through infrastructures of care they built for themselves and others; and in the ways Type 1 diabetics navigate the material culture of insulin pump treatment and the politics of diabetic care as both compulsory and liberating. Thinking across their research on chronic and crip technologies, this panel interrogates what it means, and is, to care for oneself and others in relation to the feel of navigating technologized, datafied, and materially marked lives and the systems and communities that shape their very possibilities. Building on Hamraie and Fritsch’s (2019) conception of “crip technoscience” and capacious notions of care articulated by folks in our research, panelists examine how chronic and disabled lives are lived between “enclosed regimes of self-care” with their individualizing models of selfhood and “collective communal care” frameworks (Sharma, 2017, para. 20, para. 4) that our research shows often require new ways of thinking about and better sourcing social and technological infrastructures that are centered around chronic and crip ways of living.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.059 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".