Bad medicine : a critique of health care discourse on Aboriginal populations in Canada / by Andrew Lodge.
Bibliographic record
Abstract
For many years, voices from Aboriginal populations living in Canada have called \nattention to the fact that significant challenges exist to their pursuit of good health and \nwell-being. Unsurprisingly, perhaps, this trend is not unique to the boundaries of \nCanada, but is instead reproduced in other parts of the world by different indigenous \npeoples. In Canada, Aboriginal peoples endure poorer health outcomes in a majority of \nmeasures when compared with figures for the overall population. In some instances, \nthese differences are profound. \nInterestingly, these challenges are visible elsewhere in the Canadian context, in other \narenas where Aboriginal peoples come into contact with dominant societal institutions. \nThese challenges have been most clearly documented with respect to the entire edifice of \njustice (for a compelling overview of the relationship between the justice system see, for \ninstance, Ross, 1992, Report of the Aboriginal Justice Inquiry, 1991 and the Royal \nCommission on Aboriginal Peoples [RCAP], 1996). \nThere have been no shortage of calls from within the health care establishment to address \nthis situation and efforts have been made to reform curricula and even institutions to the \nend of rectifying the problem, largely under the guise of the multicultural paradigm Canada so proudly espouses. These efforts have focused on improving relations between \nAboriginal and non-Aboriginal communities through increased tolerance, understanding, \nand so on.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.082 | 0.073 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.021 | 0.038 |
| Insufficient payload (model declined to judge) | 0.007 | 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".