Bibliographic record
Abstract
All Indigenous peoples across the globe have experienced multiple historical colonial aggression and assaults. In Canada and the USA for example, education was used as a tool of oppression for Indigenous peoples through residential school. Child welfare, health and health care, and forced land relocation are also sites of intensive and invasive harms. Health services continue to be a site of systemic and personal oppression for Indigenous peoples across Canada and the world (Reading 2013). For many years, Indigenous peoples have faced discrimination and racism when accessing biomedical health care. Implementation of colonization in Canada, Australia, New Zealand, and elsewhere, have been well documented to adversely influence aspects of health in many Indigenous communities worldwide and linked to high rates of mental health, education, and employment challenges (see Loppie & Wein, 2009; Mowbray, 2007; Paradies, Harris, & Anderson, 2008); these traumas are rooted attempts in cultural extermination and deep-set pains in regard to identity and well-being (Stout & Downey, 2006; Thurston & Mashford-Pringle, 2015).
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 | 0.025 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".