Managing Ethical Dilemmas in Community-Based Participatory Research With Vulnerable Populations
Bibliographic record
Abstract
This article describes two ethical dilemmas encountered by our research team during a project working with undocumented immigrants in Toronto, Canada. This article aims to be transparent about the problems the research team faced, the processes by which we sought to understand these problems, how solutions were found, and how the ethical dilemmas were resolved. Undocumented immigrants are a vulnerable community of individuals residing in a country without legal citizenship, immigration, or refugee status. There are more than half a million undocumented immigrants in Canada. Through an academic-community partnership, a study was conducted to understand the experiences of undocumented immigrants seeking health care in Toronto. The lessons outlined in this article may assist others in overcoming challenges and ethical dilemmas encountered while doing research with vulnerable communities.
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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 | 0.506 | 0.332 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.052 | 0.085 |
| Scholarly communication | 0.028 | 0.018 |
| Open science | 0.008 | 0.035 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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".