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Record W2624992973 · doi:10.1177/0020731416684325

Trade, Labour Markets and Health

2016· article· en· W2624992973 on OpenAlexaff
Courtney McNamara, Ronald Labonté

Bibliographic record

VenueInternational Journal of Health Services · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsWork (physics)Public healthGeneral partnershipEconomicsHealth policyBusinessInvestment (military)Public economicsLabour economicsHealth carePolitical scienceEconomic growthMedicinePolitics

Abstract

fetched live from OpenAlex

Previous analyses indicate that there are a number of potentially serious health risks associated with the Trans-Pacific Partnership (TPP). The objective of this work is to provide further insight into the potential health impacts of the TPP by investigating labour market pathways. The impact of the TPP on employment and working conditions is a major point of contention in broader public debates. In public health literature, these factors are considered fundamental determinants of health, yet they are rarely addressed in analyses of trade and investment agreements. We therefore undertake a prospective policy analysis of the TPP through a content analysis of the agreement's Labour Chapter. Provisions of the Chapter are analyzed with reference to the health policy triangle and four main areas through which labour markets influence health: power relations, social policies, employment conditions and working conditions. Findings indicate that implementation of the TPP can have important impacts on health through labour market pathways. While the Labour Chapter is being presented by proponents of the agreement as a vehicle for improvement in labour standards, we find little evidence to support this view. Instead, we find several ways the TPP may weaken employment relations to the detriment of health.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.595
Threshold uncertainty score0.831

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.028
GPT teacher head0.339
Teacher spread0.311 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations19
Published2016
Admission routes1
Has abstractyes

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