Making Connections: Leopold’s Land Health, Indigenous Ways of Knowing, Social-Ecological Resilience, and One Health
Bibliographic record
Abstract
Abstract Leopold’s land health concept provides a holistic view of the relationship between human health and ecosystem health. Significantly, a similar view is found in the traditional wisdom of Indigenous peoples, as exemplified by the concept of “healthy country, healthy people”. A contemporary formulation of land health is provided by social-ecological resilience, which refers to integrated complex adaptive systems that include social (human) and ecological (biophysical) subsystems in a two-way feedback relationship. Resilience is dynamic and provides the tools for dealing with change and disasters proactively. Building resilience and using social/institutional learning for adaptive governance are relevant to One Health, as are Indigenous concepts for the maintenance of healthy relationships between humans and the ecosystem. One Health Impact Statement The One Health approach centers on the interdependence between the health of humans, animals, and the ecosystem. However, relevant ecosystem concepts have not been developed sufficiently for a comprehensive One Health approach, as pointed out in the editorial by Zinsstag and Crump. The basic idea behind One Health not only goes back many decades in Western science but also to the environmental perception and wisdom of traditional societies. The consideration of these connections is relevant for the development of a solid theoretical basis for One Health.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".