Reimagining Community and Knowledge: Emerging Directions in Participatory Research
Bibliographic record
Abstract
There is a global resurgence in the field of participatory research and community engagement. The world health system crisis post pandemic, geo-political inequalities, social exclusion and ecological degradation expose the limits of development and the dominant positivist paradigm of research which captures the social world as ‘fact’ intact. Traditional top-down research known for upward accountability to donors and institutions of power, seldom take stance and support downward and lateral accountability (towards participants/ community and the researchers). There is a growing recognition of the need for approaches which focuses on participants lived experiences, share power and build research with rather than on communities. In the emerging context, learning institutions, social justice philanthropists, and grassroots community organisations are increasing calling for models of inquiry that are not only methodologically rigorous but also socially just and locally rooted. This special edition on “Participatory Research and Community Engagement” captures this pivotal moment in the evolution of research practice. The papers presented here reflect a cross-section of the dynamic efforts to respond to the complex glocal social issues through inclusive, contextually grounded, and collaborative approaches. Together, they represent a shift- as Thomas Kuhn puts it from extractive model of knowledge production and ‘development-by-accumulation’ view of science to those that prioritises co-creation, accountability and relational ethics.
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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 | 0.346 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.020 | 0.236 |
| Scholarly communication | 0.058 | 0.091 |
| Open science | 0.010 | 0.042 |
| Research integrity | 0.025 | 0.026 |
| Insufficient payload (model declined to judge) | 0.007 | 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".