Harried by Harding and Haraway: student–mentor collaboration in disability studies
Bibliographic record
Abstract
Exploring the friendships of disabled youth in forthcoming doctoral research raised many unsettling questions. Members of academic and disability communities thoughtfully asked how the researcher could legitimately understand, interpret and represent the experiences of disabled youth. The initial impulse was to rely on nearly two decades of clinical practice with children and youth with disabilities; however, the futility of this strategy quickly surfaced. Uncertainty about how to proceed arose. A colleague and mentor suggested that a careful reading of Sandra Harding, Donna Haraway and Mats Alvesson and Kaj Sköldberg might provide the conceptual tools required to address these concerns. This paper presents a student’s stumbling, hesitant and sometimes ‘harried’ attempts to grapple with their unfamiliar arguments while simultaneously exploring tentative connections with disability studies. The evolutionary cycle of queries, responses and reflections from a series of e‐mails demonstrate a transition in thinking about research and representation.
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 | 0.052 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.034 | 0.026 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".