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Record W2265905021

Transfer Pricing Within The North American Pharmaceutical Industry: Has There Been A Structural Shift In Risk?

2005· article· en· W2265905021 on OpenAlexaboutno aff
Jamal Hejazi

Bibliographic record

VenueSSRN Electronic Journal · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsnot available
Fundersnot available
KeywordsMultinational corporationBusinessPharmaceutical industryOrder (exchange)Transfer pricingCorporationMarketingIntellectual propertyIndustrial organizationFinanceLaw
DOInot available

Abstract

fetched live from OpenAlex

Patent protection within the North American pharmaceutical industry has enabled pharmaceutical companies in the past to obtain a degree of exclusivity with respect to a particular market. This exclusivity is needed to provide drug companies with significant profits on winner drugs in order to offset the losses associated with the many failures that occur during the long developmental process. Laws in Canada that are designed to protect intellectual property have not been strong enough and have forced pharmaceutical companies to perform valuable research and development activity in other tax jurisdictions such as the United States. Within North America, a multinational pharmaceutical company is often structured in such a way that core research and development is performed in the U.S. with the marketing activity being done in Canada. However, changes to laws that seemingly favour generic drug manufacturers, coupled with regulations placed on marketing efforts have seeming shifted the risk towards the research and development effort and away from the marketing activity. This should alter the way tax authorities view the profits associated with the activities of various members of a multinational corporation with marketing receiving more routine returns and the core research and development activity receiving a greater potential for higher profits (and losses) due to increasing risks.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.006
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 head0.053
GPT teacher head0.251
Teacher spread0.198 · 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.

Study designObservational
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

Citations0
Published2005
Admission routes1
Has abstractyes

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