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Record W312868857 · doi:10.1177/009885880603200204

How Parallel Trade Affects Drug Policies and Prices in Canada and the United States

2006· article· en· W312868857 on OpenAlexaffabout
Aidan Hollis, Peter Ibbott

Bibliographic record

VenueAmerican Journal of Law & Medicine · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsThe King's UniversityInstitute of Health EconomicsUniversity of Calgary
Fundersnot available
KeywordsScrutinyDrug pricesEconomicsInternational tradePharmaceutical industryInternational economicsCommercial policyBusinessPublic economicsPolitical scienceLawBiotechnology

Abstract

fetched live from OpenAlex

U.S. consumers and Canadian pharmacies have rushed to take advantage of the opportunity presented by price differences in patented pharmaceuticals. This rapidly growing parallel trade has brought the Canadian and U.S. systems for determining pharmaceutical pricing under increased scrutiny, and the pressure for change seems to be building. This paper examines why parallel trade in pharmaceuticals has grown and considers some of the policy options confronting both countries. To do this we begin by identifying similarities and differences in the Canadian and U.S. regulatory frameworks governing trade in pharmaceuticals. While the differences are considerable, we show that they are not the only reason for the emergence of the price disparity that has fuelled the growth in parallel trade. In particular, we argue that the price discrimination strategies of pharmaceutical manufacturers and exchange rate fluctuations have played an underappreciated role. Following this, we examine the claims that this trade represents a threat to American and Canadian interests, and find that there are good reasons for policy makers on both sides of the border to be concerned. Unchecked, this rising trade presents a threat to R&D funding and continued Canadian consumer access to pharmaceutical products.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.809
Threshold uncertainty score0.542

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 studies0.0000.001
Scholarly communication0.0000.000
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 head0.017
GPT teacher head0.243
Teacher spread0.226 · 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 designTheoretical or conceptual
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
Published2006
Admission routes2
Has abstractyes

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