Bibliographic record
Abstract
Introduction Canada is commonly seen as a progressive country with its multicultural model, its tolerance for diversity and its sensitised human rights legislation (Elliot and Bonauto, 2005). Although Canada can be commended for the progress it has made in each of these areas, one need only scratch the surface to expose the inequalities and inequities that lie beneath. Lesbian, gay, bisexual and trans (LGBT) populations are a clear example of a people who were once unrecognised culturally, neither tolerated nor accepted socially, and completely devoid of inclusion in human rights legislation and its ensuing protections in Canada. The last 45 years have seen momentous shifts in each of these areas, so much so that it has produced a near utopian veneer that serves to mask continuing forms of oppression and micro-aggressions that simmer from below. Despite the elevation of Canada's LGBT communities as a recognised population that makes up part of the multicultural fabric of the land, with near full recognition of human rights protection in legislation, LGBT people fall woefully behind the general population with regard to health and wellbeing. HIV/AIDS continues to be the illness-based focus that the Canadian state gives varying degrees of support to, barely recognising the broader health and wellness issues, needs and concerns that affect LGBT Canadians. Two models have informed health policy in Canada (with international influence), which have progressively focused on diversity, given the multicultural make-up of the country, with varying success: • the world-renowned ‘Health Promotion’ model (Government of Canada, 1974), which focused on achieving a healthy lifestyle; • the internationally regarded Population Health model (Health Canada, 1998, 2001), which more explicitly identifies diverse populations and attempts to address the social determinants of health (SDoH) (Public Health Agency of Canada, no date). Neither have completely addressed LGBT people as a population that experiences health inequalities: the former was critiqued for its lack of attention to structural differences; and the latter fell short, due to its over-emphasis on determinants of health at the expense of ‘social’ aspects – becoming mired in unsatisfactory notions of ‘health cause and effects’ (Orsini, 2007).
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 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".