Ethical Considerations: Research with People in Situations of Forced Migration
Bibliographic record
Abstract
Research can contribute to better understanding of the forced migration experience to inform policy and programming, but it can also cause inconvenience and harm to research respondents.[1] In situations of forced migration, the stakes are particularly high because of precarious legal status, unequal power relations, far-reaching anti-terrorism legislation, and the criminalization of migration. In response, the Canadian Council for Refugees, York University’s Centre for Refugee Studies, and the Canadian Association for Refugee and Forced Migration Studies collaborated to complement established ethical principles with specific ethical considerations for research with people in situations of forced migration. This document highlights our guiding principles and applies the ethical concepts of voluntary, informed consent; respect for privacy; and cost-benefit analysis. It is of relevance to anyone involved in gathering information—whether in an academic or community setting—and those who are asked to take part in 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 | 0.245 | 0.352 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.044 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.030 | 0.034 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".