Bibliographic record
Abstract
This article brings together critical disability scholarship and personal narrative, sharing the author’s pandemic story of disruption, caregiving, grief, burnout, cancer, and post-operative fatigue. It offers critical reflection on the limits of the neoliberal academy and possibilities for practicing liberatory politics within it, posing two central questions: What does it mean to crip time and centre care as an arts-based researcher? What might a commitment to honouring crip time based on radical care do for the author and their scholarship, and for others aspiring to conduct reworlding research? This analysis suggests that while committing to “slow scholarship” is a form of resistance to ableist capitalist and colonial pressures within the academy, slowness alone does not sufficiently crip research processes. Crip time, by contrast, involves multiply enfolded temporalities imposed upon (and reclaimed by) many researchers, particularly those living with disabilities and/or chronic illness. The article concludes that researchers can commit to recognizing crip time, valuing it, and caring for those living through it, including themselves, not only/necessarily by slowing down. Indeed, they can also carry out this work by actively imagining the crip futures they are striving to make along any/all trajectories and temporalities. This means simultaneously transforming academic institutions, refusing internalized pressures, reclaiming interdependence, and valuing all care work in whatever time it takes.
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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.126 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".