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Record W2200313555

Une alternative à la réforme du financement des services de garde au Québec

2015· preprint· fr· W2200313555 on OpenAlexaboutno aff
Nicholas‐James Clavet, Jean‐Yves Duclos

Bibliographic record

VenueRePEc: Research Papers in Economics · 2015
Typepreprint
Languagefr
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesArt
DOInot available

Abstract

fetched live from OpenAlex

Cet article évalue les effets de deux réformes du financement des services de garde sur les familles et les finances publiques. Nous estimons que la réforme récemment mise en place par le gouvernement du Québec entraîne une diminution de 169 M$ du revenu disponible des familles avec des enfants de 5 ans et moins ; 48% des familles sortent perdantes de cette réforme. La réforme alternative récemment recommandée par la Commission de révision permanente des programmes et la Commission d’examen sur la fiscalité québécoise entraîne une diminution (substantiellement plus faible) de 14, 7 M$ (plutôt que de 169 M$) du revenu disponible des familles ; une proportion importante (79, 1%) des familles ne sont pas pénalisées par cette réforme (97% des familles du premier quintile en sortent d’ailleurs gagnantes, contrairement à 0% pour la récente réforme) et le gouvernement en retire aussi un revenu net supérieur de 215, 3 M$ (plutôt que de 204 M$). Il ressort ainsi que cette réforme alternative est plus avantageuse que celle récemment mise en place, à la fois pour les familles québécoises et pour le gouvernement provincial — elle augmente substantiellement la contribution du gouvernement fédéral au financement des services de garde et aligne davantage cette contribution au niveau de celle dont profitent les familles des autres provinces canadiennes.

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)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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: Other · Consensus signal: none
Teacher disagreement score0.069
Threshold uncertainty score0.268

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.000

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 head0.052
GPT teacher head0.358
Teacher spread0.306 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2015
Admission routes1
Has abstractyes

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