Care haunts, hurts, heals: The promiscuous poetics of queer crip Mad care
Bibliographic record
Abstract
Care is a dirty word for many in our communities. "Caregiving" has become a euphemism for often-indifferent, under-funded labor that is done to our bodies to (barely) enable our continued survival. Care is a dirty word in many of our leftist-feminist communities. Care work is a classification of highly gendered and racialized labor that remains largely unpaid, underpaid, and deeply devalued. Care is a dirty word in our Mad, disability, queer activist communities. "Taken into care" often refers to indefinite confinement, forced extraction from communities and families, and the removal of one's right to self-determination. Is care even worth reclaiming? In this creative duo-ethnography, a Mad fat femme and a crip ill non-binary queerdo wander through various moments when care has most impacted our lives, our relationships, and our communities. We have each held one another with care on the precipice of dying. Our bodies have shouldered the love-labor of care in the most intimate, exigent, and banal of moments: consensual and playful medication reminders, postsurgery tampon changing, literally squeezing out another's breath to stay alive-and then repeating-hundreds of times an evening. We have also experienced care that was much too careful, and anything but full of care. We have shared care promiscuously with our crip and Mad (arts) communities in ways that have been life affirming, life changing, sometimes life making, other times life threatening. Through this wandering with ideas, moments, and communities, we reflect upon multiple dimensions of Mad, queer, crip care. Whom is the caring for? What is our care about? And how can our care be given or giving, taken or shared, offered, enabled, and co-created with flourish?
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.047 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".