North-South research collaboration during complex global emergencies: Qualitative knowledge production and sharing during COVID-19
Bibliographic record
Abstract
Large multinational teams of academics and activist-practitioners that span the Global North-South divide have become common in qualitative research because of the reliance of field of peace and conflict studies on “local” knowledge and expertise. Complex global emergencies, such as the COVID-19 pandemic, present the opportunity to (re)shape and (re)consider these endeavors in key some ways. This article focuses on the involvement of South-based activist-practitioners in three large North-South collaborations, one pre-pandemic (Beyond Words: Implementing Latin American Truth Commission Recommendations), one ongoing when the pandemic began (Gender, Justice, and Security Hub), and one launched during the pandemic (Truth Commissions and Sexual Violence: African and Latin American Experiences). Drawing on center-periphery framework, we adopt an autoethnographic approach, to reflect on how the pandemic has not only reinforced existing structural and institutional asymmetries through reduced funding, professional uncertainty, and personal loss and insecurity but also added some new ethical concerns. This reality has tested both our capacity and commitment to work toward the decolonization of knowledge in the field. In making this argument, we seek to contribute to the discussion on research ethics and the politics of knowledge production and sharing in qualitative peace and conflict 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 | 0.099 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.036 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".