‘I will play this tokenistic game, I just want something useful for my community’: experiences of and resistance to harms of peer research
Bibliographic record
Abstract
Hiring peer researchers – individuals with lived experience of the phenomenon under study – is an increasingly popular practice. However, little research has examined experiences of peer research from the perspectives of peer researchers themselves. In this paper, we report on data from a participatory, qualitative research project focused on four intersecting communities often engaged in peer research: mental health service user/consumer/survivor; people who use drugs; racialized; and trans/non-binary communities. In total, 34 individuals who had worked as peer researchers participated in semi-structured interviews. Transcripts and interviewer reflections were analyzed using a participatory approach. Many participants reported exposure to intersecting forms of systemic oppression (racism, transphobia, ableism, and classism, among others) and disparagement of their identities and lived experiences, both from other members of the research team and from the broader institutions in which they were working. Peer researchers described being required to perform academic professionalism, while simultaneously representing communities that were explicitly or implicitly denigrated in the course of their work. Practices of resistance to these harms were evident throughout the interviews, and participants often made strategic decisions to permit themselves to be tokenized, out of the expectation of promised benefits to their communities. However, additional harms were often experienced when these benefits were not realized. These findings point towards the need for a more reflexive and critical approach to the use of peer research.
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 | 0.042 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.029 | 0.055 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".