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Record W2942713958 · doi:10.7202/1058593ar

TAUX MARGINAUX EFFECTIFS D’IMPOSITION : UNE COMPARAISON QUÉBEC-ONTARIO

2019· article· fr· W2942713958 on OpenAlexaffvenueabout
Arnaud Blancquaert, Nicholas‐James Clavet, Jean‐Yves Duclos, Bernard Fortin, Steeve Marchand

Bibliographic record

VenueL Actualité économique · 2019
Typearticle
Languagefr
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsCenter for Interuniversity Research and Analysis on OrganizationsUniversité Laval
Fundersnot available
KeywordsPolitical scienceHumanitiesGeographyArt

Abstract

fetched live from OpenAlex

Cet article présente un portrait pour 2014 des taux marginaux effectifs d’imposition (TMEI) sur le revenu de travail et des taux d’imposition à la participation (PTR) au marché de l’emploi au Québec et en Ontario. L’objectif est de mieux comprendre et de comparer l’impact de la fiscalité et des transferts sociaux sur les incitations au travail dans ces deux provinces. Le système québécois, relativement à celui de l’Ontario, engendre des TMEI et des PTR élevés attribuables à la réduction rapide des transferts avec le revenu de travail ainsi qu’à une plus grande générosité des transferts pour les familles. Les TMEI québécois sont particulièrement élevés et variables entre 0 et 50 000 $. Le TMEI des familles biparentales aux revenus d’environ 20 000 $ dépasse même les 125 %; 40 % de ces familles biparentales font face à un TMEI qui dépasse 50 %. La moyenne des PTR dans l’ensemble de la population est de 41 %. Toutefois, pour les individus de 65 ans et plus ayant des revenus faibles, le système québécois engendre des TMEI et des PTR significativement inférieurs à ceux qui découlent du système ontarien.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.863
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.005

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 head0.160
GPT teacher head0.366
Teacher spread0.206 · 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; both teacher heads agree on what is shown here.

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
Published2019
Admission routes3
Has abstractyes

Explore more

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