Underground Patchworks of Access: Migrant Health Activism in Ontario and the Emotional Work of Storytelling and Deservingness
Bibliographic record
Abstract
This dissertation examines how Ontario migrant health activists use de/bordering strategies to negotiate healthcare access for individuals with precarious status. It focuses on the emotional work involved in this advocacy, exploring how activists navigate shifting institutional and interpersonal contexts, manage emotional challenges, and adapt their de/bordering efforts accordingly. Using discourse analysis, I draw on 47 in-depth interviews and institutional and governmental health documents. Engaging interdisciplinary literatures in bordering studies, social movement theory, and affect scholarship, I analyze how storytelling, emotions, and context shape migrant health advocacy. Activists’ stories, shaped by evolving contexts and emotional responses, inform their de/bordering strategies. Their emotional work is sustained through coalition-building and solidarity, which help them cope with distress and build relationships that in turn shape their advocacy. I argue that by telling stories rooted in emotional experiences and shifting contexts, activists iteratively construct non-binary understandings of deservingness and challenge dominant discourses and power structures. Their de/bordering strategies evolve across socio-political and emotional contexts, drawing on storytelling, informal knowledge-sharing, and coalition-building as both advocacy and support. This dissertation contributes to bordering scholarship by offering empirical and theoretical insights into how de/bordering strategies are enacted in migrant health advocacy and how emotional and contextual shifts shape activists’ work. I examine how activists negotiate healthcare access while navigating exclusionary immigration and health regimes. In doing so, I bring de/bordering as a concept into conversation with broader dynamics of differential inclusion and expand the literature by developing a framework attuned to the complexity of deservingness assessments in this field.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.021 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".