Bibliographic record
Abstract
In order to fulfill a broader vision of health and wellness, the World Health Organization (WHO) 2014–2023 strategy for global health has outlined a culturally sensitive blending of conventional biomedicine with traditional forms of healing. At the same time, scientists working in various fields—from anthropology and ecology to biology and climatology—are validating and demonstrating the utility of Indigenous knowledge. There is a misperception that Indigenous peoples are in need of Westernized science in order to “legitimize” our knowledge systems. The Lancet Planetary Health Commission report calls for the “training of indigenous and other local community members” in order to “help protect health and biodiversity” (p. 2007). Such calls have merit but appear authoritarian when they sit (unbalanced) without equally loud calls for the training of (socially dominant) westernized in-groups by Indigenous groups “in order to help protect health and biodiversity.” The problems of planetary health are both profound and complex; solutions can be found in a greater understanding of the self and the universe and the land as a medicine place. The following message was delivered as part of a keynote at the inVIVO Planetary Health Conference in Canmore, Alberta, Canada—20 points of consideration for a planetary health science in its pure, raw form, on behalf of the Indigenous elders.
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 | 0.016 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.058 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".