Bibliographic record
Abstract
This paper explores Sara Ahmed’s Queer Phenomenology from a disability studies perspective. In addition to her emphasis on race and desire, I ask how we might use Ahmed’s queer, cultural phenomenology to ask about the sociomaterial basis of disablement, reflecting on the interactive emergence of these subjectivities more generally. In the first section of this paper, I examine¬¬ the three main chapters in Ahmed’s important book. I then ask what Ahmed might have asked, if she had explored disability therein. Next, I turn to some phenomenological disability studies, interrogating how subjectivity is put to work in the shared world, rather than universally accorded to all persons at all times. In the final section of this paper, I return to the basis of the phenomenological project itself, and ask what this revised version of subjectivity means for the phenomenology of Heidegger and Husserl, with an eye to future work.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.074 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".