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Inclusive Anti-poaching? Exploring the Potential and Challenges of Community-based Anti-Poaching

2017· article· en· W4312627314 on OpenAlexafffund
Francis Massé, Alan H. Gardiner, Rodgers Lubilo, Martha Themba

Bibliographic record

VenueSouth African Crime Quarterly · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of CanadaYork University
KeywordsPoachingLaw enforcementAcknowledgementWildlifeEnforcementPsychological interventionPolitical scienceLocal communityEnvironmental planningGeographyBusinessEnvironmental resource managementLawEcologyComputer securityEconomicsPsychology

Abstract

fetched live from OpenAlex

In acknowledgement that the largely (para)militarized approach to anti-poaching has its limitations, alternative approaches to conservation law enforcement are being sought. One alternative focuses on including people from local communities in anti-poaching, what we call inclusive anti-poaching. Using a case study of a community scout programme from southern Mozambique, located adjacent South Africa’s Kruger National Park, we examine the potential of a community scout initiative to move towards a more inclusive and sustainable approach to anti-poaching and conservation. While highlighting its challenges and potential drawbacks, we argue that including local people into conservation law enforcement efforts can help address poaching and problematic aspects of current anti-poaching measures. However, to be a genuine and sustainable alternative, community ranger programmes must be part of a broader shift towards developing local wildlife economies that benefits local communities as opposed to supporting pre-existing anti-poaching interventions.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
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
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.042
GPT teacher head0.252
Teacher spread0.210 · 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.

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

Citations5
Published2017
Admission routes2
Has abstractyes

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