The Merits and Pitfalls of Participatory Action Research: Navigating Tokenism and Inclusion with Lived Experience Members
Bibliographic record
Abstract
This paper explores the merits and pitfalls of involving people with lived and living experiences of a phenomenon of interest (e.g., poverty, hunger, housing deprivation) in Participatory Action Research (PAR). As researchers who have conducted PAR and community-based research for several years, the authors have gained deep insight into the value of having lived/living experience members in PAR projects, as well as the challenges attendant to such work. Using a collaborative autoethnographic methodology, this paper provides an overview of PAR, including its purposes and objectives. Aiming to move past tokenistic inclusion, issues associated with meaningful participation, including relational (e.g., issues of power), ethical (e.g., risks of participation), emotional (e.g., research triggers), economic (e.g., remunerating contributions and financially supporting participation), representational (e.g., whose perspectives are advanced), and structural barriers (e.g., time, technological connectivity, etc.) are discussed using concrete examples. Bringing together people who may hold disparate perspectives, community ties, worldviews, and visions associated with a research undertaking can create challenges, but not including those who experience the phenomenon of study can create even more challenges.
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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 | 0.408 | 0.269 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.021 | 0.128 |
| Scholarly communication | 0.030 | 0.038 |
| Open science | 0.007 | 0.037 |
| Research integrity | 0.008 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".