Ethical Dilemmas of Conducting Research Among Precarious Status Migrants: Research Ethics Boards and Beyond
Bibliographic record
Abstract
In Canada, research is governed by national ethical guidelines and standards enforced by institutional research ethics boards (REBs) to protect vulnerable populations, such as temporary migrant farmworkers. However, the rigid and inflexible application of these procedures often creates significant barriers for social science researchers striving to promote social justice for this population and may lead to antagonism between researchers and REB chairs. Institutional ethics frameworks frequently fail to account for the embeddedness of research within rural agricultural communities and the critical role these communities play in shaping ethical decision-making. When REBs overlook these relational and contextual dynamics, they impose requirements that can obstruct meaningful, on-the-ground ethical engagement. To illustrate these tensions, we focus on three challenges, namely, participant recruitment strategies, consent procedures, and protection from trauma. To avoid misunderstandings and antagonism, we call for a paradigm shift toward understanding research ethics as relations, that is, inherently embedded within complex networks of relationships, emphasizing iterative models of consent developed through ongoing negotiations between researchers, participants, and other community members. Bridging the gap between ethical research in theory and practice requires a relational paradigm that recognizes the need to mitigate hostilities and antagonism and emphasizes the importance of collaboration and dialogue – not only among academics, migrants, and community stakeholders but also between researchers and REB members.
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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 | 0.517 | 0.403 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.038 | 0.122 |
| Scholarly communication | 0.037 | 0.021 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.021 | 0.028 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".