Part of the Bioethics and Medical Ethics Commons, Community-based Research Commons, Nutrition Commons, Politics and Social Change Commons, and the Social Welfare Commons
Bibliographic record
Abstract
Research: Good or Bad? Canada went through a tough discussion in July 2013 when it was revealed that between 1942 and 1952 unethical and harmful research was conducted on Aboriginal peoples, most of whom were children. Beyond simply condemning unethical research with Indigenous populations, we need to examine why this happened and understand what the implications and lessons are for "policy research " moving forward. Policy research is a powerful tool when conducted in the proper way. We must never lose sight of the reason we are engaged in the activity: to improve well-being through the improvement of understanding that leads to change. The research process must, itself, be part of the positive process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.100 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.022 | 0.179 |
| Scholarly communication | 0.039 | 0.022 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.026 | 0.037 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".