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Record W2974997700

Comparative Analysis of the NDCs of Canada, the European Union, Kenya and South Africa from an Equity Perspective : a research report funded by the Swedish Energy Agency

2019· book· en· W2974997700 on OpenAlexaboutno aff
Guy Cunliffe, Christian Holz, Kennedy Mbeva, Pieter Pauw, Harald Winkler

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2019
Typebook
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsEquity (law)NegotiationPolitical scienceEuropean unionPublic administrationBusinessInternational tradeLaw
DOInot available

Abstract

fetched live from OpenAlex

In the lead-up to COP21 in Paris, 2015, all Parties to the UNFCCC were invited to communicate their intended nationally determined contributions (INDCs), which could include information on how the Party considers its INDC is fair and ambitious (1/CP.20, para 14). The same information to accompany nationally determined contributions (NDCs) was included in the Paris decision adopted at COP21. While there is extensive literature on climate equity, comparatively little research exists on equity in NDCs. Analysis of equity in NDCs is important, firstly because NDCs represent a unique step in UN climate negotiations, in that they are universal and applicable to all Parties, and secondly because NDCs are formulated bottom-up. As countries determine their own priorities and ambitions they self-differentiate their responsibilities to address climate change. This research report examines equity considerations in the domestic processes for the preparation of NDCs. Four Parties are examined in this analysis, selected based on having widely varying domestic contexts and processes for NDC preparation: Canada, Kenya, the European Union and South Africa

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)0.004
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.766
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.003
Research integrity0.0000.001
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 head0.154
GPT teacher head0.313
Teacher spread0.159 · 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 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

Citations4
Published2019
Admission routes1
Has abstractyes

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