ICIRAS: Research and reconciliation with indigenous peoples in rural health journals
Bibliographic record
Abstract
Aim: We aim to promote discussion about an Indigenous Cultural Identity of Research Authors Standard (ICIRAS) for academic journal publications. Context: This is based on a gap in research publishing practice where Indigenous peoples' identity is not systematically and rigorously flagged 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 against the world's Indigenous peoples. Reflecting on these broader movements, the editorial teams of three rural health journals—the Australian Journal of Rural Health, the Canadian Journal of Rural Medicine, and Rural and Remote Health—recognised that Indigenous peoples' identity could be embedded in authorship details. Approach: An environmental scan (through a cultural safety lens where Indigenous cultural authority is respected, valued, and empowered) of literature was undertaken to detect the signs of inclusion of Indigenous peoples in research. This revealed many ways in which editorial boards of Journals could systematically improve their process so that there is ‘nothing about Indigenous people, without Indigenous people’ in rural health research publications. Conclusion: Improving the health and wellbeing of Indigenous peoples worldwide requires high quality research evidence. The philosophy of cultural safety supports the purposeful positioning of Indigenous peoples within the kaleidoscope of cultural knowledges as identified contributors and authors of research evidence. The ICIRAS is a call-to-action for research journals and institutions to rigorously improve publication governance that signals “Editing with IndigenUs and for IndigenUs”.
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 | 0.224 | 0.390 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.021 | 0.036 |
| Scholarly communication | 0.035 | 0.022 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".