Beyond the Partnership Debate: Localizing Knowledge Production in Refugee and Forced Migration Studies
Bibliographic record
Abstract
Abstract There is a growing recognition in refugee and forced migration studies that research partnerships, especially those that cross geographies of the global North and global South, are both a blessing and a potential curse. They are a blessing as they encourage new approaches to the co-creation of knowledge, build solidarity networks, and leverage support for scholars based in the global South. But they can also be a curse as they typically function within and can inadvertently reproduce deeply embedded structures of inequality. Drawing on the results of a review of forced displacement research centres based in the global South and interviews with the directors of these centres, this article encourages a shift from focusing on research partnerships to an approach that supports the localization of knowledge production in refugee and forced migration studies. This approach seeks to change the structures of knowledge production, including direct funding to researchers and research centres based in the global South, an emphasis on the transfer of power to researchers in the South, a recognition of the diverse forms and sources of knowledge produced within the field, and an appreciation for the diverse understandings of success and impact across contexts.
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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 | 0.095 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.016 | 0.076 |
| Scholarly communication | 0.033 | 0.047 |
| Open science | 0.004 | 0.037 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".