Co-production of Knowledge in Forced Migration Studies: An Interdisciplinary Analysis of Challenges and the Possibilities for the Emergence of Best Practices
Bibliographic record
Abstract
Although 80% of the world’s refugees are hosted in the global South, the majority of research in forced migration studies is produced and disseminated by researchers and institutions in the global North. The asymmetry of power in knowledge production is not limited to the North-South divide, but it also occurs between researchers and their research subjects (refugees). This type of hegemonic scholarship plagues all aspects of the research process, from the choice of methodology and research questions to the publication and dissemination of findings, and reflects the privileged position of global North scholars. Given the geopolitical context within which refugees find themselves situated, addressing these challenges is especially urgent as refugees, impacted by both displacement and immobility, are the most impacted by the consequences of policy decisions, yet are furthest removed from the processes of policy creation. This paper attempts to demonstrate how understandings of the temporal, spatial, and embodied aspects of displacement and forced migration may be enhanced through a renewed approach to research that excavates hidden agency and power hierarchies, as well as the challenges and limitations of pursuing such diverse methods to knowledge production. It conducts an interdisciplinary analysis of knowledge production, drawing on the theoretical insights of development studies, feminist studies, and Indigenous studies to make visible structures of power and oppressive practices within forced migration research. It examines what sorts of best practices are available and makes recommendations for how they can be wielded to navigate and dismantle the dominant structures of knowledge production in forced migration research.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Science and technology studies Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
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 | 0.133 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.029 | 0.120 |
| Scholarly communication | 0.047 | 0.040 |
| Open science | 0.006 | 0.038 |
| Research integrity | 0.008 | 0.008 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".