Indigenous community engagement requirements for academic journals
Bibliographic record
Abstract
This commentary emerged from an Indigenous research ethics and governance gathering and a scoping review completed by a diverse team of Indigenous and non-Indigenous scholars, which includes some of the co-authors of this article. A lack of detail regarding whether and how community engagement was carried out and reported in the context of published Indigenous health research in the Atlantic region of Canada were identified. This commentary builds on this work as well as other published works that emphasize the need to further ensure that Indigenous research is community based if not community led. Moreover, this commentary lends support to important changes to journal submission requirements regarding Indigenous health research submissions recently made at the Canadian Journal of Public Health through the work of Senior Editor Dr Janet Smylie and colleagues.
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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 | 0.438 | 0.772 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.035 | 0.023 |
| Scholarly communication | 0.055 | 0.028 |
| Open science | 0.012 | 0.030 |
| Research integrity | 0.053 | 0.037 |
| Insufficient payload (model declined to judge) | 0.020 | 0.015 |
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".