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Record W2118313885 · doi:10.2190/mt8d-h4ec-jkme-3kd3

International Trade Regulation and Publicly Funded Health Care in Canada

2001· article· en· W2118313885 on OpenAlexafffundabout
Aleck Ostry

Bibliographic record

VenueInternational Journal of Health Services · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsUniversity of British Columbia
FundersHealth CanadaCanadian Institute for Advanced Research
KeywordsSanctionsMultinational corporationInternational tradeBusinessHealth careGovernment (linguistics)Public healthPrivate sectorEconomic growthEconomicsFinancePolitical scienceLawMedicine

Abstract

fetched live from OpenAlex

The World Trade Organization (WTO) creates new challenges for the Canadian health care system, arguably one of the most "socialized" systems in the world today. In particular, the WTO's enhanced trade dispute resolution powers, enforceable with sanctions, may make Canadian health care vulnerable to corporate penetration, particularly in the pharmaceutical and private health services delivery sectors. The Free Trade Agreement and its extension, the North American Free Trade Agreement, gave multinational pharmaceutical companies greater freedom in Canada at the expense of the Canadian generic drug industry. Recent challenges by the WTO have continued this process, which will limit the health care system's ability to control drug costs. And pressure is growing, through WTO's General Agreement on Trade in Services and moves by the Alberta provincial government to privatize health care delivery, to open up the Canadian system to corporate penetration. New WTO agreements will bring increasing pressure to privatize Canada's public health care system and limit government's ability to control pharmaceutical costs.

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)0.005
metaresearch head score (Gemma)0.015
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: Empirical · Consensus signal: none
Teacher disagreement score0.721
Threshold uncertainty score0.836

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.006
Science and technology studies0.0140.008
Scholarly communication0.0130.002
Open science0.0020.005
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0140.000

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 head0.016
GPT teacher head0.329
Teacher spread0.313 · 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
GenreEmpirical

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

Citations9
Published2001
Admission routes3
Has abstractyes

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