Can scientists and knowledge keepers sit comfortably together? An Indigenous physician’s reflections on a decade of participatory research into First Nations nutrition, environment and health
Bibliographic record
Abstract
The author, an Indigenous physician, offers his reflections on the history of scientific research with Indigenous People and its past role in ethical breaches and excesses of colonialism, as a backdrop to the relatively recent advances in collaborative, community-based participatory research involving First Nations and Inuit in Canada. The First Nations Food, Nutrition and Environment Study (2008-2018), introduced in this Special Issue, is presented as an example of an ethical space that was sustained for a decade to collaboratively develop new knowledge by First Nations and scientists working together, respectfully and inspired by shared interest. A short overview of twelve articles of the Special Issue is provided and characterized as creating a previously inaccessible picture of the modern diets of First Nations, along with the suite of environmental factors that are present in food and water in and around communities. Ultimately, the author hopes that Canadian society can set the table with Indigenous Peoples and respectfully set opinions onto each other and do this over and over again. With Canada already being a multicultural and pluralistic society, adding Indigenous realities into the mix only respects and honours the Indigenous roots of this country.
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 | 0.043 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.052 | 0.073 |
| Scholarly communication | 0.022 | 0.026 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.021 | 0.049 |
| Insufficient payload (model declined to judge) | 0.002 | 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".