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Record W2890175290 · doi:10.26686/pq.v12i1.4576

The Paris Climate Change Agreement: text and contexts

2016· article· en· W2890175290 on OpenAlexaff
Adrian Macey

Bibliographic record

VenuePolicy Quarterly · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsDiplomacyAgreementPolitical scienceState (computer science)Climate changeLawPoliticsComputer science

Abstract

fetched live from OpenAlex

When French foreign minister Laurent Fabius brought down the gavel on the Paris Agreement on 12 December 2015, the international community reached a goal that had eluded it for six years: an updated and universal climate change agreement. It owed much to France’s diplomacy over the preceding 12 months, together with efficient, firm and innovative handling of the conference itself. Fundamental to the success of the Conference of the Parties (COP21) was the commitment at all levels from President Hollande down to engage with the broadest range of parties and non-state actors. The fruits of France’s engagement were nowhere more apparent than in the small island states’ comment in the final plenary that this was the first time they felt they had been listened to at a COP.

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)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.076
Threshold uncertainty score0.182

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0140.012
Scholarly communication0.0180.005
Open science0.0020.005
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0150.002

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 head0.029
GPT teacher head0.330
Teacher spread0.301 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2016
Admission routes1
Has abstractyes

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