Bibliographic record
Abstract
This is an accepted article with a DOI pre-assigned that is not yet published.The people of Sagamok Anishnawbek First Nation (SAFN)are located on the North shore of the Georgian Bay, on Lake Huron, situated in what is now known as Mid-Northern Ontario, Canada. They have lived here since time immemorial. Over the past 174 years, SAFN has been subject to environmental and cultural exploitation via logging and pulp and paper mill by-products, mining including uranium mining, proposed nuclear waste sites, and unauthorized industrial waste sites, which has been contested by the Anishnaabek. The exploitive colonial extraction of natural resources from the community and the surrounding area represents both a historical and a current struggle to resolve. Here we explore the determinants of health impacting Indigenous communities and to identify areas of strength plus those in need of improvement as it relates to broader understandings of community wellbeing. We propose that these determinants of health help to understand environmental and cultural exploitation that exists from colonial extraction of natural resources while First Nations, like SAFN, express community resilience by utilizing environmental stewardship, cultural continuity, and self-determination to move into a more sustainable future that respects traditional (Anishnaabek) concepts.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.927 | 0.867 |
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".