Bibliographic record
Abstract
Abstract Interconnected health between humans, animals, and the land is a concept held by many Indigenous Peoples. Systematic problems such as climate change, zoonotic diseases, environmental contamination, and commercialized food systems threaten the collective health of creation. This article explores Indigenous perspectives of One Health through the concepts of well-being taught from Cree and Secwepemc perspectives. The ethics of living a “good life” are embedded in both Secwepemc and Cree languages and hold a responsibility to be in good relation with your food system. Being well is more than meeting nutritional requirements for survival, but also caring for the connections between the land, food systems, culture, and knowledge. Indigenous scholars have critiqued One Health research for not fully encompassing collective health and breaking relationships into parts, such as the narrowed focus on zoonotic disease transmission. The Union of British Columbia Indian Chiefs released a statement rejecting factory farming due to its disrespect toward animals and its impacts on the environment. Given the disrespect, oppression, and violence commercialized farms possess, they cannot be a part of living a good life. If One Health research focuses on the relationships between the land and food systems, we could address the cause of the imbalance rather than mitigating its symptoms. One Health impact statement Indigenous peoples have been practicing the philosophies encompassed in One Health through their worldviews and land laws since time immemorial. Relational eating, as explored in this article, and One Health are inherently interconnected, as relational eating incorporates all aspects of self (spiritual, emotional, mental, and physical); therefore, it requires people of all backgrounds and knowledge systems to achieve. Systematic problems such as climate change, zoonotic diseases, and the resulting issues in our food systems suggest that everyone is impacted by and dependent on the land. Shifting to relational eating, as guided by Indigenous ways of knowing, doing, and being, will help all humans live a good life, where we can achieve collective health among humans, animals, and the environment.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.056 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".