The rights way forward: reconciling the right to food with biodiversity conservation
Bibliographic record
Abstract
Abstract The current paradigm of biodiversity conservation, with its continued focus on the notion of pristine nature, has resulted in the separation of humans and nature at the expense of both biological and cultural–linguistic diversity. The continued annexation of land for the cause of conservation has resulted in the curtailment of both rights and access to local and diverse food sources for many rural communities. Indigenous Peoples and local communities are fundamental to conserving biodiversity through sustainable use of nature despite repeated attempts to dispossess them from their lands, cultures and knowledge. It has been this traditional and land-based knowledge that has contributed to the conservation of biodiversity whilst also supporting healthy, diverse and nutritious diets. If we are to achieve a more just and sustainable future, we need to continue to centre conservation initiatives around rights, access and equity whilst respecting a plurality of perspectives, worldviews and knowledge systems. Here we review alternative approaches that help reconcile the right to food with biodiversity conservation, such as biocultural rights, rights-based approaches and integrated land management schemes, with the aim of identifying optimal ways forward for conservation that break away from the dichotomous view that pits people against nature and instead embrace the importance of this symbiotic relationship.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.040 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".