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Record W4403038710 · doi:10.32942/x2p34c

Pathways for transformative change in biodiversity politics: Examining the significance of the Global Biodiversity Framework’s ‘Considerations’

2024· preprint· en· W4403038710 on OpenAlexaboutno aff
Alison Hutchinson, Anthony R. Zito, Philip J.K. McGowan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsTransformative learningBiodiversityPoliticsEnvironmental ethicsPolitical scienceEnvironmental resource managementSociologyEnvironmental scienceEcologyBiologyPhilosophyLaw

Abstract

fetched live from OpenAlex

This paper examines the ‘Considerations’ that are intended to underpin the implementation of the Kunming-Montreal Global Biodiversity Framework (KMGBF). With so little time to meet the 2030 mission of transforming conservation approaches and curbing biodiversity decline, we reflect on the opportunities the Considerations present for transformative governance in biodiversity conservation. We discuss how contrasting worldviews and foundations of knowledge shape the Considerations, and inform the Framework more broadly, and highlight where areas of ambiguity between anthropocentric and nature-centred approaches arise. We contend that if the global community is to meaningfully change the trajectory of species extinctions and biodiversity loss, transformative changes are needed in the values held and expressed towards nature in political, economic, and social spheres. We conclude by suggesting implementation tools and processes to help foster the meaningful integration of the more boundary-pushing Considerations in wider biodiversity governance and practice.

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)0.035
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.025
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0100.059
Scholarly communication0.0230.020
Open science0.0020.012
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0070.000

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 head0.077
GPT teacher head0.249
Teacher spread0.172 · 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 designTheoretical or conceptual
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

Citations0
Published2024
Admission routes1
Has abstractyes

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