Position statement: research and reconciliation with Indigenous Peoples in rural health journals
Bibliographic record
Abstract
It's time to plant a flag in the White soil of academic journal publishing and declare, 'This discourse includes the cultural voices of Indigenous Peoples'.Indigenous Peoples are almost invisible as academic authors in rural health journals.Occasionally that indigeneity may be deduced through the institutional or organisational affiliation statements, or in the acknowledgements, or in the text of articles.Too frequently, it is not discernible in any way.In essence, Indigenous cultural identity is suppressed by the conventions of academic publishing.This sees author and subject credibility resting on Western views of provenance, including institutional affiliation, college membership, educational qualifications, and disciplinary background.This research colonialism reflects a power imbalance that must end.We, as a consortium of three rural health journals (Rural and Remote Health, Canadian Journal of Rural Medicine and Australian Journal of Rural Health), believe that cultural heritage and cultural provenance are as important as, if not more important than, Western views of provenance, regarding any research involving Indigenous Peoples.We commit to developing a textual flag to signal to readers that the author is Indigenous, as denoted in our
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 | 0.051 | 0.169 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.027 | 0.015 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.084 | 0.047 |
| Insufficient payload (model declined to judge) | 0.025 | 0.014 |
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".