Bibliographic record
Abstract
Disabled people/people with disabilities (DP/PWDs) need allies in general and subject matter specific allies in particular given the many problems they face in their daily lives, as it is, for example, evident in the language of the UN Convention on the Rights of Persons with Disabilities. Numerous actions are required from allies of, for example, DP/PWDs and other marginalized groups. Authentic allies are often expected to take on activist roles, yet activism carries the risk of activist burnout, which in turn places allies at risk of ally burnout. Despite this, ally burnout is rarely discussed in the literature, and, to date, there are no studies that specifically examined ally burnout in the context of allyship to and by DP/PWDs. The aim of this study was to deepen our understanding of the factors and topics that heighten the risk of ally burnout, including both non-disabled individuals acting as allies to DP/PWDs and DP/PWDs acting as allies to others. In this study, our participants included eighty-seven critical disability studies students. Critical disability studies students were chosen because they see themselves as allies of DP/PWDs and want to make a positive difference in the lives of DP/PWDs, including disabled students. We used an online survey using the Qualtrics platform as our tool to obtain the data. Our participants identified many factors that can lead to ally burnout and indicated many actions needed to decrease the danger of ally burnout of non-disabled people being allies of DP/PWDs and DP/PWDs being allies of others.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".