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Record W2805473668 · doi:10.3390/su10061846

Effective Biodiversity Conservation Requires Dynamic, Pluralistic, Partnership-Based Approaches

2018· article· en· W2805473668 on OpenAlexaff
Michael C. Gavin, Joe McCarter, Fikret Berkes, Aroha Te Pareake Mead, Eleanor J. Sterling, Ruifei Tang, Nancy J. Turner

Bibliographic record

VenueSustainability · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Cultural Studies in Latin America and Beyond
Canadian institutionsUniversity of VictoriaUniversity of Manitoba
FundersMax-Planck-Institut für MenschheitsgeschichteNational Science Foundation
KeywordsConvention on Biological DiversityGeneral partnershipBiodiversityBiodiversity conservationConservation psychologyEnvironmental resource managementEnvironmental planningSustainable developmentVisionEnvironmental ethicsPolitical scienceSociologyEcologyEconomicsGeographyBiologyLaw

Abstract

fetched live from OpenAlex

Biodiversity loss undermines the long-term maintenance of ecosystem functions and the well-being of human populations. Global-scale policy initiatives, including the Convention on Biological Diversity, have failed to curb the loss of biodiversity. This failure has led to contentious debates over alternative solutions that represent opposing visions of value-orientations and policy tools at the heart of conservation action. We review these debates and argue that they impede conservation progress by wasting time and resources, overlooking common goals, failing to recognize the need for diverse solutions, and ignoring the central question of who should be involved in the conservation process. Breaking with the polarizing debates, we argue that biocultural approaches to conservation can guide progress toward just and sustainable conservation solutions. We provide examples of the central principles of biocultural conservation, which emphasize the need for pluralistic, partnership-based, and dynamic approaches to conservation.

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 imitation

Not 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.

metaresearch head score (Codex)0.036
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.189

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0140.055
Scholarly communication0.0260.022
Open science0.0030.025
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.015
GPT teacher head0.245
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations184
Published2018
Admission routes1
Has abstractyes

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