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Record W4320922466 · doi:10.3389/fcosc.2023.989019

Implications of Russia’s invasion of Ukraine for the governance of biodiversity conservation

2023· article· en· W4320922466 on OpenAlexaff
Eduardo Gallo‐Cajiao, Nives Dolšak, Aseem Prakash, Taej Mundkur, Paul G. Harris, Ronald B. Mitchell, Nick C. Davidson, Birgita D. Hansen, Bradley K. Woodworth, Richard A. Fuller, Melissa R. Price, Nicky Petkov, Volker Mauerhofer, Tiffany H. Morrison, James Watson, Sayam U. Chowdhury, Christoph Zöckler, Oscar Widerberg, Ding Yong, Daniel Klich, Vitaliy Smagol, John Piccolo, Duan Biggs

Bibliographic record

VenueFrontiers in Conservation Science · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Biological Research in Conflict Zones
Canadian institutionsInstitute for Circumpolar Health Research
FundersCedar Tree Foundation
KeywordsBiodiversityCorporate governanceBiodiversity conservationPolitical scienceInternational regimeEnvironmental planningEnvironmental resource managementGeographyBusinessEcologyBiologyEconomicsLaw

Abstract

fetched live from OpenAlex

Maintaining peace and conserving biodiversity hinge on an international system of cooperation codified in institutions, but Russia’s invasion of Ukraine brings recent progress to a crossroads. Against this backdrop, we address some implications of Russia’s invasion of Ukraine for the governance of biodiversity conservation both within and beyond Russia. The Russian invasion of Ukraine threatens the governance system for biodiversity conservation, as it pertains to Russia and beyond, due to three interacting factors: (i) isolation of Russia from the international system, (ii) halt and delay of international cooperation, and (iii) changes in international and domestic policy priorities. We recommend making the existing international system of governance for conserving biodiversity more resilient and adaptable, while aligning security agendas with biodiversity conservation goals.

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)0.002
metaresearch head score (Gemma)0.003
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: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.005
Scholarly communication0.0040.002
Open science0.0000.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.066
GPT teacher head0.294
Teacher spread0.228 · 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

Citations30
Published2023
Admission routes1
Has abstractyes

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