Democratic encounters? Epistemic privilege, power, and community-based participatory action research
Bibliographic record
Abstract
The literature suggests that community-based participatory research holds the potential to democratize and decolonize knowledge production by engaging communities and citizens in the research enterprise. Yet this approach, and its associated claims, remain under theorized, particularly as to how power circulates between and among academic and community knowledge work/ers. This paper puts forth a postcolonial analysis of participatory techniques that sustain academe’s epistemic privilege through producing, subordinating and assimilating difference; claiming authenticity and voice; and dislocating collaborative knowledge work from the historical, political, social and embodied conditions in which it unfolds. Postcolonial readings of community-based participatory action research offer a powerful theoretical framework for interrogating the divide between the discursive claims and material practices that undermine this democratic project. Drawing on critical reflections on two community-based participatory action research projects, this paper offers modest proposals toward (re)placing community-based knowledge work/ers in space, time and bodies. Although this paper presents a critique of community-based participatory action research, it is not in pursuit of revealing “bad” participatory praxis or recuperating a better practice, but rather seeks to open up dialogue on the circulation of power in the campus/community encounter.
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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 | 0.100 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.165 |
| Scholarly communication | 0.029 | 0.032 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".