"We have to start sounding the trumpet for things that are working": An interview with Dr. Marlene Brant-Castellano on concrete ways to decolonize research
Bibliographic record
Abstract
In 2004, Dr. Marlene Brant-Castellano published a well-received, and now widely cited article entitled "Ethics of Aboriginal research" in the inaugural issue of the Journal of Aboriginal Health. About a decade after this inspirational publication, we asked her to reflect on (1) the progress made in terms of ethics of research with aboriginal people; (2) her views on concrete ways to decolonize research; and (3) challenges yet to overcome in terms of ethical conduct of research with aboriginal people
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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 | 0.040 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.034 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.013 | 0.053 |
| Insufficient payload (model declined to judge) | 0.002 | 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".