Complicating 'the right to health care': Narratives of deservingness among im/migrants in Sault Ste. Marie, Ontario
Bibliographic record
Abstract
In the age of globalization and massive mobility, local and dynamic understandings of im/migration, health, health policy and health care are increasingly important. In recent literature, Sarah Willen and colleagues (2012) introduce the concept of health-related deservingness, bringing to the fore local, everyday “reckonings” of who deserves what and why, as opposed to the well-studied entitlement and access dimensions of health and health care. Inspired by this work and by my own experience as an im/migrant in Canada, this study explores the health-related deservingness “reckonings” of im/migrants in the Algoma region of Ontario, particularly Sault Ste. Marie. Through my analysis of various in-depth interviews and a focus group, I propose that experiences (as opposed to conceptions) of deservingness can be at least as important a determinant of im/migrants’ overall health and well-being as formal entitlement and access. Furthermore, because experiences of deservingness are individual/localized, implicit and dynamic, they are more vulnerable to contextual influences and also more ‘negotiable’. This ‘malleability’ of deservingness presents opportunities to re-think im/migrants’ agency, as well as the moral obligations and responsibilities of im/migrants and non-im/migrants alike.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.023 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".