Ethics in Forced Migration Research: Taking Stock and Potential Ways Forward
Bibliographic record
Abstract
Migration research poses particular ethical challenges because of legal precarity, the criminalization and politicization of migration, and power asymmetries. This paper analyzes these challenges in relation to the ethical principles of voluntary, informed consent; protection of personal information; and minimizing harm. It shows how migration researchers - including those outside of academia - have attempted to address these ethical issues in their work, including through the recent adoption of a Code of Ethics by the International Association for the Study of Forced Migration (IASFM). However, gaps remain, particularly in relation to the intersection of procedural and relational ethics; specific ethical considerations of big data and macrocomparative analyses; localized meanings of ethics; and oversight of researchers collecting information outside of institutional ethics boards. The paper concludes with the following recommendations: Institutional Research Ethics Boards should familiarize themselves with the particular ethical challenges in migration research, as well as available resources, such as the IASFM Code of Ethics. Ethics boards should include researchers and community representatives who are familiar with migration in reviews of related projects.Academic and training programs in migration studies should include sessions and resources on migration-specific research ethics.Nonacademic organizations, including migrant-led organizations, should provide information resources and training to their staff and clients to ensure that they understand procedural ethics requirements, relational ethical principles, as well as the rights of those asked to participate in research. Organizations conducting their own research should establish ethics review processes and relational ethics norms.A leading migration studies center or institution should map existing ethical guidelines and processes in different countries and contexts to be better aware of overlap and gaps. This mapping should take the form of an open access, interactive database, so that information can be accessible and updated in real time.Researchers should engage in more dissemination of lessons learned on ethics in migration. While there is some emerging consensus on key ethical principles for migration research, it is in their application that researchers face dilemmas. Honest reflection and sharing of these experiences will help researchers to anticipate and manage similar dilemmas they encounter while undertaking research.Researchers at all stages of their careers should not undertake migration research without having first reviewed some of the literature on ethics and migration, which is partially cited in this paper.Research centers should facilitate dialogue on ethical issues in languages other than English, particularly languages most spoken by people in migration, and by people who are underrepresented in formal ethics processes and debates, especially those with direct experience of migration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.301 | 0.189 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.024 | 0.128 |
| Scholarly communication | 0.047 | 0.123 |
| Open science | 0.008 | 0.035 |
| Research integrity | 0.046 | 0.055 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".