Vagues de capacitisme : Arrangements affectifs au temps de la COVID-19
Bibliographic record
Abstract
Drawing on critical disability studies and affect theory, this paper centers the affective arrangements that have governed the COVID-19 pandemic, and looks ahead to new articulations that might emerge in which disability and ableism structure state responses. While the last two plus years have demonstrated in powerful ways how COVID-19 is a space brimming with affective energies, they have also shown us that health emergencies can be the occasion to replay feelings about others who rest outside of the normative boundaries of healthy/unhealthy. Developing a theory of institutionalized affect that attends to ableism in a neoliberal context, we seek to contribute to an affective disability studies that can generate new meanings of pandemic time that can crip notions of normal or pathological. With the pressure to return to “normal”, to fast-forward away from lockdowns and mask mandates, and to replace bad feeling with good feelings, there are legitimate fears that the waves of optimism and care that characterized the start of the pandemic, sentiments of support for people made vulnerable by COVID-19 will recede as citizens return to the feel-good narrative of pre-COVID time (Tremain 2020). For many disabled people, that normal toggled between hypervisibility as Other and invisibility, as if the concerns of disabled people never mattered in the first place.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.081 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| 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".