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Record W4393275280 · doi:10.1002/sd.2975

Indonesia's sustainable development goals in relation to curbing and monitoring the illegal wildlife trade

2024· article· en· W4393275280 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSustainable Development · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsWildlife Conservation Society Canada
FundersRoyal Geographical SocietyOxford Brookes UniversityPeople's Trust for Endangered SpeciesGlobal Challenges Research FundCleveland Zoological SocietyBournemouth University
KeywordsWildlifeWildlife tradeSustainable developmentNatural resource economicsRelation (database)BusinessEnvironmental resource managementEnvironmental planningEconomicsGeographyEcologyBiologyComputer science

Abstract

fetched live from OpenAlex

Abstract Indonesia has committed to implement the sustainable development goals (SDG) by 2030 including the ending trafficking of protected species and addressing the illegal wildlife demand and supply. As such, there is a need for long‐term data on wild animal trade and its contribution to the wider economy. We initiated a long‐term monitoring programme of live civet trade in wildlife markets (120 surveys, 2010–2023). Civets are traded to be kept as exotic pets and to produce civet coffee and are a proxy for other high‐profile wildlife. We recorded 2289 civets of six species, including ones with strict regulations in place. Despite the trade being illegal, and contra to Indonesia's commitments as part of the SDG to curb this trade, it remained remarkably stable over time (numbers, species, prices). As such, Indonesia is not meeting its SDG targets that are related to curbing illegal wildlife trade and illicit financial flows.

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.

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 score0.355
Threshold uncertainty score0.731

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.009
GPT teacher head0.206
Teacher spread0.198 · 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