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Record W2770062549 · doi:10.1017/s0030605317001557

IUCN's encounter with 007: safeguarding consensus for conservation

2017· article· en· W2770062549 on OpenAlexaff
Simon N. Stuart, Shaikha Al Dhaheri, Elizabeth L. Bennett, Duan Biggs, Andrew Bignell, Onnie Byers, Rosie Cooney, John Donaldson, Holly Dublin, Hilde Eggermont, Barbara Engels, Basile van Havre, Michael Hoffmann, Masahiko Horie, Jon Hutton, Ashok Khosla, Frédéric Launay, Caroline Lees, Georgina M. Mace, Julia Marton-Lefèvre, Vivek Menon, Russell A. Mittermeier, Tamar Pataridze, Miguel Pellerano, Ramón Pérez Gil, John G. Robinson, Jon Paul Rodrı́guez, Aroha Te Pareake Mead, Spencer W. Thomas, Marina von Weissenberg

Bibliographic record

VenueOryx · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsIUCN Red ListSafeguardingVettingPolitical scienceGeographyIUCN protected area categoriesEcologyLawBiologyMedicine

Abstract

fetched live from OpenAlex

Abstract A controversy at the 2016 IUCN World Conservation Congress on the topic of closing domestic ivory markets (the 007, or so-called James Bond, motion) has given rise to a debate on IUCN's value proposition. A cross-section of authors who are engaged in IUCN but not employed by the organization, and with diverse perspectives and opinions, here argue for the importance of safeguarding and strengthening the unique technical and convening roles of IUCN, providing examples of what has and has not worked. Recommendations for protecting and enhancing IUCN's contribution to global conservation debates and policy formulation are given.

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)0.170
metaresearch head score (Gemma)0.180
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.170
Threshold uncertainty score0.900

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1700.180
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0180.049
Scholarly communication0.0280.018
Open science0.0040.025
Research integrity0.0250.021
Insufficient payload (model declined to judge)0.0100.001

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 head0.016
GPT teacher head0.241
Teacher spread0.225 · 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 designQualitative
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

Citations11
Published2017
Admission routes1
Has abstractyes

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