Bibliographic record
Abstract
This article builds on the critical disability theory of affordances that I have been developing through ethnographic inquiries and the notion of “microactivist affordances,” by which I mean micro and everyday acts of world building with which disabled people literally make up, and at the same time make up for, whatever affordance fails to readily materialize in their environments. Drawing from fieldwork in Turkey and Quebec with people who have chronic pain and mobility-related disabilities, I explore how microactivist affordances emerge, not through the complementarity of a single perceiver and the world but through the complementarity of multiple perceivers and the world, within the particular material conditions of living with disability. Taking into account the sociality of my interlocutors’ microactivist affordances and their, after Ginsburg and Rapp, “disability worlds,” I propose the notion of “people as affordances” as a way to describe how people can enable the emergence of, or directly become, affordances for one another, especially where no other affordances exist. I then explore the various forms that “people as affordances” may take and that allow people to create access by their own means, and the socialities within which that access creation may—or may fail to—materialize. Finally, I suggest that “people as affordances” can provide new ways of understanding care that I, after Mia Mingus’s work, conceptualize as “care intimacy.”
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.045 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".