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Record W578208340 · doi:10.1017/cbo9781107238985

Linking Global Trade and Human Rights

2014· book· en· W578208340 on OpenAlexaff
Daniel Drache, Saradindu Bhaduri, Sol Picciotto, Ernst‐Ulrich Petersmann, Jorge Heine, Tomer Broude, Lesley A. Jacobs, Kuldeep Mathur, Amit Ray, Kathryn Hochstetler, Ronald Labonté, Matias E. Margulis, Adelle Blackett, Ljiljana Biuković, Pitman B. Potter, Sarah Biddulph, Chang-Hee Lee

Bibliographic record

VenueCambridge University Press eBooks · 2014
Typebook
Languageen
FieldSocial Sciences
TopicHuman Rights and Development
Canadian institutionsYork University
Fundersnot available
KeywordsHuman rightsCognitive reframingGlobalizationGlobal governancePolitical sciencePoliticsChinaCorporate governanceInternational tradeEconomicsLaw

Abstract

fetched live from OpenAlex

During the global economic crisis of 2008, countries around the world used national policy spaces to respond to the crisis in ways that shed new light on the possibilities for linkages between international trade and human rights. This book introduces the idea of policy space as an innovative way to reframe recent developments in global governance. It brings together a wide-ranging group of leading experts in international law, trade, human rights, political economy, international relations, and public policy who have been asked to reflect on this important development in globalization. Their multidisciplinary contributions provide explanations for the changing global landscape for national policy space, clearly illustrate instances of this change, and project the future paths for policy development in social and economic policy spaces, especially with reference to linkages between international trade and human rights in countries from the Global North as well as Brazil, China, and India.

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 score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.015
Scholarly communication0.0090.007
Open science0.0000.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0180.002

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 head0.023
GPT teacher head0.240
Teacher spread0.217 · 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

Citations11
Published2014
Admission routes1
Has abstractyes

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