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Record W2977486763 · doi:10.1017/s1474745619000351

NAFTA 2.0: The Greenest Trade Agreement Ever?

2019· article· en· W2977486763 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWorld Trade Review · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsFree trade agreementInternational tradeAgreementPrecautionary principlePolitical scienceSettlement (finance)BusinessFree tradeEcology

Abstract

fetched live from OpenAlex

Abstract The renegotiation of what US President Trump called ‘the worst trade deal ever’ has resulted in the most detailed environmental chapter in any trade agreement in history. The USMCA mentions dozens of environmental issues that its predecessor, the North American Free Trade Agreement (NAFTA), overlooked, and in line with contemporary US practice, brings the vast majority of environmental provisions into the core of the agreement, and subjects these provisions to a sanction-based dispute settlement mechanism. It also jettisons two controversial NAFTA measures potentially harmful to the environment. However, this paper argues that the USMCA only makes limited contributions to environmental protection. It primarily replicates most of the environmental provisions included in recent agreements, and only introduces three unprecedented environmental provisions. Moreover, it avoids important issues such as climate change, it does not mention the precautionary principle, and it scales back some environmental provisions related to multilateral environmental agreements.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.883
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.007

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 head0.097
GPT teacher head0.262
Teacher spread0.165 · 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