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Record W2338500431 · doi:10.1002/fee.1251

Navigating governance networks for community‐based conservation

2016· review· en· W2338500431 on OpenAlexafffund
Steven M. Alexander, Mark Andrachuk, Derek Armitage

Bibliographic record

VenueFrontiers in Ecology and the Environment · 2016
Typereview
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of CanadaInternational Development Research Centre
KeywordsCorporate governanceAction (physics)Network governancePublic relationsPolitical scienceCommunity-based conservationSocial network analysisEnvironmental resource managementConservation psychologyEnvironmental planningKnowledge managementBusinessEcologyGeographyComputer scienceBiologyBiodiversityEconomicsSocial capital

Abstract

fetched live from OpenAlex

Governance networks can facilitate coordinated action and shared opportunities for learning among conservation scientists, policy makers, and communities. However, governance networks that link local, regional, and international actors just as often reflect social relationships and arrangements that can undermine conservation efforts, particularly those concerning community‐level priorities. Here, we identify three “waypoints” or navigational guides to help researchers and practitioners explore these networks, and to inspire them to consider in a more systematic manner the social rules and relationships that influence conservation outcomes. These waypoints encourage those engaged in community‐based conservation ( CBC ) to: (1) think about the networks in which they are embedded and the constellation of actors that influence conservation practice; (2) examine the values and interests of diverse actors in governance, and the implications of different perspectives for conservation; and (3) consider how the structure and dynamics of networks can reveal helpful insights for conservation efforts. The three waypoints we highlight synthesize an interdisciplinary literature on governance networks and provide key insights for conservation actors navigating the challenges of CBC at multiple scales and levels.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.017
GPT teacher head0.232
Teacher spread0.215 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations160
Published2016
Admission routes2
Has abstractyes

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