Working with all nations and all relatives in feeding the future
Bibliographic record
Abstract
Most unprecedented changes and challenges to planetary health that include earth and human health, are attributed to short-sighted policies and systemic barriers. Standardized and top-down approaches of development that often dominate through limited, persuasive, and extractive euro-centric perspectives often dominate in Turtle Island and most colonial regions of the world. Food and food-sustaining relatives (land, water, plants, animals, micro-habitats) which are central to planetary health, are negatively impacted and threatened by these human pressures, which have severe implications for our ability to feed current and future generations (FAO et al., 2023; Planetary Health Alliance, n.d.). Many international agencies (including those affiliated with the United Nations), food systems scholars, grassroots organizations, and community members are grappling with the very imminent challenges of addressing the alarmingly high level of food insecurity in Turtle Island (Council of Canadian Academics, 2014; Fieldhouse & Thompson, 2012) and the global South (Kuhnlein et al., 2013).
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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.014 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 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".