Indigenous Cultural Identity of Research Authors Standard: research and reconciliation with Indigenous Peoples in rural health journals
Bibliographic record
Abstract
The Indigenous Cultural Identity of Research Authors Standard (ICIRAS) is based on a gap in research publishing practice where Indigenous peoples' identity is not systematically and rigorously recognised in rural health research publications. There are widespread reforms, in different research areas, to counter the reputation of scientific research as a vehicle of racism and discrimination. Reflecting on these broader movements, the editorial teams of three rural health journals - Rural and Remote Health, the Australian Journal of Rural Health, and the Canadian Journal of Rural Medicine - adopted a policy of 'Nothing about Indigenous Peoples, without Indigenous Peoples'. This meant changing practices so that Indigenous Peoples' identity could be embedded in authorship credentials - such as in the byline. An environmental scan of literature about the inclusion of Indigenous Peoples in research revealed many ways in which editorial boards of journals could improve their process to signal to readers that Indigenous voices are included in rural health research publication governance. Improving the health and wellbeing of Indigenous peoples worldwide requires high-quality research evidence. This quality benchmark needs to explicitly signal the inclusion of Indigenous authors. The ICIRAS is a call to action for research journals and institutions to rigorously improve research governance and leadership to amplify the cultural identity of Indigenous peoples in rural health research.
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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 | 0.254 | 0.422 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.027 | 0.044 |
| Scholarly communication | 0.031 | 0.019 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".