Transnational Migration and Research Ethics
Bibliographic record
Abstract
In conducting critical participatory inquiries with youth across the Canada-U.S. border, we – Karamjeet Dhillon and Kaitlin Popielarz, along with Jasmine Ulmer – have encountered ethical complexities in research involving migration. Here, we discuss the ethical complexities in two cases, each from a participatory dissertation. In the first case, which involves youth with refugee status in Windsor, Ontario, Canada, several participants in Dhillon’s study requested to be identified by name so that others could know who they are, what they have experienced, and what they can contribute. By way of response, the research team co-created with participants in different ways to honor their identities. In the second case, Popielarz also contended with issues of anonymity, albeit of a different sort. What became an action research project with youth organizers in Detroit, Michigan, U.S.A., had initially been conceptualized in a different setting with different participants; however, when officers from the U.S. Immigrations and Customs Enforcement (ICE) positioned themselves outside schools in Southwest Detroit – an area in which many immigrant students and their families reside – changes were made to maintain the privacy, confidentiality, and safety of community members without documentation. Together, these cases indicate the need to navigate macroethics and microethics when conducting critical participatory inquiries with transnational populations, particularly around issues of anonymization, confidentiality, and consent. Further, these cases illustrate the importance of attending to how participants self-identify. When individuals are treated as generic policy labels, they are given much less than they are owed with respect to their individuality.
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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 | 0.318 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.031 | 0.180 |
| Scholarly communication | 0.025 | 0.018 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.016 | 0.025 |
| 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; 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".