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Record W2317774834 · doi:10.1177/1468018116637209

Trade agreements and labour standards clauses: Explaining labour standards developments through a qualitative comparative analysis of US free trade agreements

2016· article· en· W2317774834 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueGlobal Social Policy · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal trade, sustainability, and social impact
Canadian institutionsnot available
FundersUniversität SalzburgRijksuniversiteit GroningenLondon School of Economics and Political ScienceMcGill University
KeywordsArgument (complex analysis)EconomicsCausality (physics)Industrial relationsLabour economicsFree tradeLabour lawInternational tradeInternational economics

Abstract

fetched live from OpenAlex

Whereas a number of studies have been conducted to investigate causal relations between individual conditions (e.g. trade relations and labour standards), there is a lack of consensus among practitioners and scholars about the conditions that favour or cause labour standards improvements and, specifically, it is still unclear whether the increasing pervasiveness of Free Trade Agreements (FTAs) is conducive to enhancing labour conditions. The aim of this study is to shed light on whether labour clauses in FTAs are conducive to better labour standard practices, whether the content of a clause makes a difference, and whether changes have anything to do with other (external) pressures that play a role in changing labour standards. The main argument of the article is that FTAs do not play a determinant role in improving labour standards in signatory states. The analysis is done by looking at 13 FTAs signed by the United States with 19 countries. The United States is chosen because of its relatively extensive collection of FTAs including different conditions on labour standards. The empirical dataset is analysed with Qualitative Comparative Analysis (QCA) method, which permits to trace the combined effect of independent variables rather than to focus on the direct and individual causality with each of them.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
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 head0.048
GPT teacher head0.387
Teacher spread0.339 · 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