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Record W3195986056 · doi:10.3389/fmars.2021.709423

Confronting Complex Accountability in Conservation With Communities

2021· article· en· W3195986056 on OpenAlexafffund
Katherine M. Crosman, Gerald G. Singh, Sabine Lang

Bibliographic record

VenueFrontiers in Marine Science · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsFisheries and Oceans CanadaUniversity of British ColumbiaMemorial University of Newfoundland
FundersEarthLab, University of WashingtonCanada First Research Excellence FundOcean Nexus Center, EarthLab, University of WashingtonOcean Frontier InstituteUniversity of Washington
KeywordsAccountabilityCommunity-based conservationPublic relationsSustainabilityBusinessReputationWork (physics)Environmental resource managementLocal communityPolitical scienceEnvironmental planningEcologyGeographyEconomicsEngineering

Abstract

fetched live from OpenAlex

Increasingly, conservation organizations are conducting conservation activities with local communities. Many conservation organizations now position their work as contributing to sustainable development initiatives, and local involvement in conservation is understood to increase conservation and sustainability success. Aside from communities, however, conservation organizations are accountable to funders and partners, and values and priorities vary across actor type. Mismatched goals combine with power imbalances between conservation actors, and create decision-making conflict throughout conservation processes, from objective setting through implementation and evaluation. As a result, communities may lose local decision-making power or face new negative consequences, trust in organizational/community partnerships may be undermined, and conservation organizations’ reputations (and the reputation of the sector as whole) may suffer. In this commentary we point out processes and conditions that can lead conservation organizations to privilege accountability to funders and others over accountability to communities, thereby undermining community-level success. We follow with suggestions for how funders, conservation organizations and others may improve community engagement and community-level outcomes, and improve their reputations in general and in their work with communities, by actively leveraging accountability to the community and involving local community members in decision-making.

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)0.001
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 score0.037
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.216
Teacher spread0.197 · 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

Citations16
Published2021
Admission routes2
Has abstractyes

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