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Record W3022538074 · doi:10.1186/s12992-020-00565-4

USMCA 2.0: a few improvements but far from a ‘healthy’ trade treaty

2020· article· en· W3022538074 on OpenAlexaffabout
Ronald Labonté, Deborah Gleeson, Courtney McNamara

Bibliographic record

VenueGlobalization and Health · 2020
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Healthcare and Medical Tourism
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComplaintTreatyPoliticsCompetition (biology)BusinessPublic economicsInternational tradePolitical scienceEconomicsInternational economicsLaw

Abstract

fetched live from OpenAlex

The USMCA (NAFTA 2.0), although signed over a year ago, went through several months of renegotiation of certain of its new rules that the Democrat-controlled US Congress wanted altered or strengthened. In December a 'Protocol of Amendment' was agreed upon and signed by the three Parties (the USA, Mexico, and Canada). A number of tough, new measures governing pharmaceuticals were revised or deleted, making it potentially easier for generic competition and lower drug costs in all three countries. Rules on protection of labour rights were also strengthened, lowering the threshold at which a complaint of unfair labour practices could be initiated. Procedures for investigating such a complaint or resolving a formal dispute were also improved. Similar procedural improvements were made on measures affecting environmental protection. These Protocol agreements are more health-positive than health-negative, and in the case of pharmaceuticals are of significant impact. Overall, however, these amendments are simply a political fine-tuning of the agreement. Concerns raised in our earlier health impact assessment of the USMCA, notably how the agreement's regulatory reforms reduce public health policy flexibilities, remain. The agreement continues to subordinate known or potential health costs of many of its measures to dubious claims of aggregate economic gains. Moreover, these gains, if materialized, are likely to accrue to those atop the income/wealth hierarchies in all three nations.

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)0.000
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.410
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.127
GPT teacher head0.449
Teacher spread0.322 · 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

Citations26
Published2020
Admission routes2
Has abstractyes

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