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Record W3011613487 · doi:10.3389/ffgc.2020.00029

Forest Conservation, Rights, and Diets: Untangling the Issues

2020· article· en· W3011613487 on OpenAlexafffund
Winy Vasquez

Bibliographic record

VenueFrontiers in Forests and Global Change · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsUniversity of British Columbia
FundersUniversity of British ColumbiaUnited States Agency for International Development
KeywordsNature ConservationNatural resource economicsPolitical scienceEnvironmental ethicsEnvironmental resource managementAgroforestryEnvironmental scienceEconomicsEcologyBiologyPhilosophy

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score0.623

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.213
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations39
Published2020
Admission routes2
Has abstractyes

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