(Dis‐)Empowering Immigrants in Canada: Political Advocacy of Immigrant and Minority Organisations Promoting Cultural Diversity in the Health Care Sector
Bibliographic record
Abstract
This article starts from the theoretical assumption that successful multicultural integration of immigrants and ethnic minorities is critically dependent on providing them with opportunities to engage in public debate and in policy‐making. Empowering cultural groups is important in particular to the traditionally vulnerable individuals in these groups, such as women. The argument is applied to Canada, a country whose turn to multicultural policies and an accompanying ethos of appreciating cultural diversity have created new opportunities for immigrant and minority groups to advocate for a pluri‐cultural opening of the health care system. These opportunities can be found in state‐monitored forms of community outreach and partial inclusion in the policy process, as well as in grassroots initiatives from immigrant or minority associations or professional networks. Promoting cultural diversity in the Canadian health care sector and empowering minorities in the system are depicted as challenging and, in spite of considerable progress over the last decade, successful primarily with respect to some exemplary practices and initiatives. Examples are drawn from the complex political fight of women with an immigrant or minority background for inclusion, diversity and social justice in the health care system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.035 | 0.006 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".