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Record W3014623406 · doi:10.7202/1068367ar

Comment la condamnation d’une forme de concurrence fiscale peut en favoriser d’autres ?1

2020· article· fr· W3014623406 on OpenAlexaffvenue
Lyne Latulippe, Julie St-Cerny-Gosselin

Bibliographic record

VenuePolitique et Sociétés · 2020
Typearticle
Languagefr
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Dans le cadre du projet sur l’érosion de la base d’imposition et le transfert des bénéfices (Base Erosion and Profit Shifting – BEPS) lancé en 2013, l’Organisation de coopération et de développement économiques (OCDE) a spécifiquement exprimé des préoccupations concernant les régimes préférentiels pour le revenu généré par la propriété intellectuelle et a recommandé que ces régimes requièrent que les bénéficiaires poursuivent des activités économiques réelles pour éviter qu’un régime soit assimilé à une pratique fiscale dommageable. L’adoption par plusieurs pays de régimes de propriété intellectuelle conformes à la recommandation de l’OCDE illustre l’effet de substitution entre les formes de concurrence fiscale et pourrait accroître la pression sur d’autres pratiques de concurrence fiscale.

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)0.011
metaresearch head score (Gemma)0.031
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 score0.018
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.007
Scholarly communication0.0070.005
Open science0.0010.005
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0130.001

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 head0.075
GPT teacher head0.371
Teacher spread0.296 · 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".

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Citations0
Published2020
Admission routes2
Has abstractyes

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