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Record W2304354486 · doi:10.1017/cbo9781316157763

NAFTA and Sustainable Development

2015· book· en· W2304354486 on OpenAlexaffabout
Hoi L. Kong, L. Kinvin Wroth, Geoffrey Garver, Giselle Davidian, Paolo Solano, Montserrat Rovalo, Leslie Welts, P. Aarne Vesilind, Laurie J. Beyranevand, Betsy Baker, Katia Opalka, Nicole Schabus, Freedom-Kai Phillips, Avidan Kent, Danni Liang, Sébastien Jodoin, Raúl Pacheco-Vega

Bibliographic record

VenueCambridge University Press eBooks · 2015
Typebook
Languageen
FieldEnvironmental Science
TopicInternational Maritime Law Issues
Canadian institutionsMcGill University
Fundersnot available
KeywordsRatificationIndigenousInternational tradePolitical scienceSustainable developmentChinaFree trade agreementScope (computer science)Environmental lawLatin AmericansState (computer science)Free tradeBusinessLawPoliticsEcology

Abstract

fetched live from OpenAlex

The North American Free Trade Agreement (NAFTA) and its companion agreement, the North American Agreement on Environmental Cooperation (NAAEC), provide important and often underappreciated protection for the environmental laws of the Party states: Canada, Mexico, and the United States. On the twentieth anniversary of NAFTA's ratification, this book assesses the current state of environmental protection under those agreements. Bringing together scholars, practitioners, and regulators from all three Party states, it outlines the scope and process of NAFTA and NAAEC, their impact on specific environmental issues, and paths to reform. It includes analyses of the impact of the agreements on such matters as bioengineered crops in Mexico, assessment of marine environmental effects, potential lessons for China, climate change, and indigenous rights. Together, the chapters of this book represent an important contribution to the global conversation concerning international trade agreements and sustainable development.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.022
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.003
Scholarly communication0.0060.005
Open science0.0010.003
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0220.005

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 head0.011
GPT teacher head0.185
Teacher spread0.174 · 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

Citations4
Published2015
Admission routes2
Has abstractyes

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