Epistemic injustices and participatory research: A research agenda at the crossroads of university and community
Bibliographic record
Abstract
This article presents an innovative framework to evaluate participatory research. The framework, comprising both a methodology and a self-assessment tool, was developed through a participatory approach to knowledge production and mobilisation. This process took place over the last two years as we, a multidisciplinary team made up of researchers and community-based organisation members from the Groupe de recherche et de formation sur la pauvreté au Québec, were building a scientific program on social injustices and participatory research. We argue that participatory research can help provide a university-community co-constructed response to epistemic injustices embedded within the processes of knowledge production. From our perspective, the mobilisation of knowledge from the university and the community, initiated at the earliest stages of the creation of a research team, is part of a critical approach to the academic production of knowledge. It also constitutes a laboratory for observing, understanding and attempting to reduce epistemic injustices through building bridges between team members. The article focuses on two dimensions of the framework mentioned above: (1) The methodology we established to build co-learning spaces at the crossroads of university and community-based organisations (recruitment of a coordinator to organise and facilitate the workshops, informal and friendly meetings, regular clarification of the process and rules of operation, time for everyone to express themselves, informal preparatory meetings for those who wanted them, financial compensation where required, etc.); and (2) A self-assessment tool available in open access that we built during the process to help academics and their partners engage in a reflexive evaluation of participatory research processes from the point of view of epistemic injustices. Throughout we pay particular attention to challenges inherent in our research program and our responses, and finish with some concluding thoughts on key issues that emerged over the course of two years’ research.
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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 | 0.455 | 0.279 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.034 | 0.150 |
| Scholarly communication | 0.057 | 0.077 |
| Open science | 0.007 | 0.045 |
| Research integrity | 0.016 | 0.015 |
| 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".