Bibliographic record
Abstract
Recent research has highlighted the contributions of forests and tree-based systems to both dietary diversity and nutrition as well as agricultural production in the form of tree-based ecosystem services. Wild foods provide a significant nutritional contribution to the diets of rural dwellers, the majority of whom would be classified as some of the world’s poorest. Yet, despite the important human-forest interactions and relative degrees of forest dependency, access to much of the global forest estate is increasingly regulated under the guise of biodiversity conservation. How this restricted access plays out when the “right to food” is a deeply enshrined human right has been deeply contested, particularly with regard to land annexation. This paper outlines the critical issues related to the dietary diversity and nutrition in the context of the availability of wild foods juxtaposed with the growing call for the annexation of land for conservation. We suggest that a more integrated and equitable approach to land management that embraces both biodiversity conservation and broader food security and nutrition goals can provide multiple benefits, while mitigating local conflicts. As such, a rights-based approach to conservation and an embracing of broader landscape perspectives are possible strategies to achieve these seemingly conflicting agendas.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.046 |
| Scholarly communication | 0.010 | 0.026 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".