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Global Leadership on Biodiversity With Regard to the Montreal Conference of the Parties 15 Summit

2023· book-chapter· en· W4388140788 on OpenAlexaboutno aff
Isidore E. Agbokou

Bibliographic record

VenueAdvances in environmental engineering and green technologies book series · 2023
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsnot available
Fundersnot available
KeywordsSummitConvention on Biological DiversityAccountabilityPolitical scienceBiodiversityCorporate governanceEarth SummitMainstreamingPublic administrationConference of the partiesConventionGeographySustainable developmentEnvironmental planningBusinessLawEcologyCartographyFinance

Abstract

fetched live from OpenAlex

The 15th meeting of the Conference of the Parties (CoP 15) to the Convention on Biological Diversity held in Montreal, Canada began a new era of global biodiversity leadership. This summit brought awareness that the member states have not been able to satisfactorily achieve all 20 Aichi targets. Though it varies from state to state, progress to protect biodiversity around the world is too slow, and the level of depletion is critical. Considering the concerns raised, a new agreement has been signed. This agreement aims to protect 30% of the planet's land, coastal areas, and inland waters by 2030; halve food waste; and achieve the vision of living in harmony with nature by 2050. A monitoring and evaluation framework has also been set out. COP 15 launched a new era for a bright future for biodiversity that needs renewed leadership. The new vision, however, is born with a lag in funding. The renewed leadership also requires retailored governance functions: narrative and high-level direction-setting, integration, mainstreaming, coordination, stakeholder engagement, accountability, etc.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.751
Threshold uncertainty score0.758

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.018
GPT teacher head0.178
Teacher spread0.160 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2023
Admission routes1
Has abstractyes

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