© 2005 CMA Media Inc. or its licensors Letters
Bibliographic record
Abstract
populations The recent publication of 2 epi-demiologic studies examining Aboriginal populations1,2 raises ethical concerns. Neither study describes any consultations with First Nations or Métis community members in the for-mulation of the research questions, de-velopment of the research protocols, in-terpretation of the data or dissemination of the results. This apparent lack of consultation is particularly problematic given that both studies could have pol-icy implications that would affect First Nations and Métis individuals and com-munities. A related ethical and method-ologic concern is raised by the use of “Aboriginality ” as a risk factor in the multivariate analyses of both studies. As a health research scientist trained in the Western academic disciplines of med-icine and public health and as a Métis woman, I respect the efforts of these authors to produce much-needed infor-mation on the health of First Nations and Métis peoples. However, I have come to understand that it is only through an approach of mutual understanding, respect and partnership that academic research will be able to contribute to im-proving the health outcomes in First Na-tions, Métis and Inuit communities.3–8 “Aboriginality ” is a social construct with little grounding in the day-to-day realities of the heterogeneous groups to which it refers. Tremendous cultural, historical, socioeconomic and political diversity exists between and within these groups. What is shared is the ex-perience of colonization and the resul-tant legacy of poverty and social stres-sors. Use of this pan-ethnic term as one of several “risk ” variables, while per-haps necessary to achieve adequate study power, devalues the unique expe-
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.861 | 0.777 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".