Learning from and with community-based and participatory action research: Constraints and adaptations in a youth-peacebuilding initiative in Haiti
Bibliographic record
Abstract
Participatory action research fits well with conflict resolution and peacebuilding; it is used by scholar-practitioners as part of field-based practice efforts that contribute to transforming conflict and add to scholarly knowledge. However, as Cynthia Chataway’s analysis of a participatory action research project undertaken with the Mohawk community of Kahnawake indicated, there are considerable constraints on mutual inquiry when it occurs in settings marked by historical oppression, distrust of outsiders and internal division; these constraints require the model to respond to the community context. Drawing on this insight, this paper explores a recent collaborative, community-based research that was part of a larger youth-centered peacebuilding and security initiative in Haiti. The initiative involved partners from Canada supporting a non-governmental organization and youth in four communities to engage in action research, under the umbrella of community-based research, as part of the 26-month project. The article draws out insights on ways in which the community-based research approach adapted to the conflict context, and reflects on the ways in which this form of engaged scholarship adds to knowledge in conflict resolution and peacebuilding.
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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 | 0.045 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.037 | 0.036 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".