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Record W2613140986

Policy Forum: The Uneasy Case for a Canadian Patent Box

2017· preprint· en· W2613140986 on OpenAlexaffvenueabout
Robin Boadway, JEAN-FRANÇOIS TREMBLAY

Bibliographic record

VenueCanadian Tax Journal/Revue fiscale canadienne · 2017
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicInnovation Policy and R&D
Canadian institutionsUniversity of OttawaQueen's University
Fundersnot available
KeywordsIntellectual propertyIncentiveBase erosion and profit shiftingExploitGovernment (linguistics)BusinessPatent ActProfit (economics)Public economicsFinanceEconomicsPatent lawPolitical scienceMarket economyLawDouble taxationTax avoidance
DOInot available

Abstract

fetched live from OpenAlex

Several countries, mainly European, have introduced patent boxes, which reduce the corporate tax rate applicable on income generated from successful research and development (R & D) expenditures. Quebec has implemented a form of patent box for manufacturing firms that generate income from patents developed in the province, and the Saskatchewan government has announced that it will follow suit. There have been calls for the federal government to introduce a patent box for Canada. The intent is to encourage firms to engage in R & D activity that leads to intellectual property and to exploit the innovations in Canada. In this article, the authors draw on the literature and experience with patent boxes elsewhere to review the case for a patent box as an element of R & D policy in Canada. They conclude that a dual approach to R & D incentives involving some combination of ex ante tax incentives and patent boxes is reasonable, especially if patent box support encompasses a variety of forms of intellectual property. At the same time, if patent boxes are introduced, they should conform to the guidelines set out by the Organisation for Economic Co-operation and Development, in the base erosion and profit shifting (BEPS) action plan, which restrict eligibility by conditions ensuring domestic development of patents.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.882
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.000
Science and technology studies0.0040.000
Scholarly communication0.0010.000
Open science0.0020.000
Research integrity0.0010.002
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 head0.081
GPT teacher head0.240
Teacher spread0.159 · 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 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

Citations1
Published2017
Admission routes3
Has abstractyes

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