Migrant Justice Research in Crisis Times: Developing Reflexive, Ethical, and Responsive Pandemic Research with Immigrant Care Workers
Bibliographic record
Abstract
Community-based participatory research (CBPR) typically prioritizes community needs in the research process, attempting to link ethical and rigorous investigation with social action. However, balancing community needs and research goals can be challenging when working with marginalized communities in times of crisis. Strategies for engaging immigrant communities in CBPR is also underexplored in academic literature. This paper examines some of these challenges by focusing on a research project with immigrant homecare workers in Manitoba, Canada, who were disproportionately impacted by COVID-19, yet largely excluded from government pandemic policy responses. The project aimed to explore these workers’ experiences and to contribute to migrant justice organizing through the research process. In this article, we present three interrelated tensions in our shifting research process: reflexive navigation of our research team members’ lived experiences and positionalities; community versus academic ethics; and timely responsiveness to shifting community priorities. We contribute to literature on CBPR with immigrant communities by articulating a reflexive migrant justice research approach amidst a crisis. This approach is developed through subversive relationship-building and intersectional solidarity with social justice community partners that disrupt dominant academic research processes.
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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 | 0.136 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.026 | 0.030 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.007 | 0.009 |
| 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".