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Record W7098007192

CIRPÉE Centre interuniversitaire sur le risque, les politiques économiques et l’emploi Cahier de recherche/Working Paper 02-05 Using a Canadian-American Natural Experiment to Study Relative Efficiencies of Social Welfare Payment Systems1

2002· article· en· W7098007192 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsPaymentWelfare
DOInot available

Abstract

fetched live from OpenAlex

at the University of Maine) for their financial support. Marceau thanks Social Sciences and Humanities Research Council of Canada for its financial support. Errors are the authors ’ responsibility. Résumé: Nous étudions l’impact de la mécanique des paiements d’aide sociale, et en particulier leur concentration dans le temps, sur les prix des denrées alimentaires que se procurent les bénéficiaires de l’aide sociale. Nous présentons tout d’abord un modèle théorique dans lequel les individus ayant des revenus relativement plus faibles s’avèrent être relativement moins mobiles. Il en découle que lorsque leurs consommateurs deviennent plus pauvres (cela se produit lorsque les bénéficiaires de l’aide sociale ont épuisé leur prestation mensuelle), les marchands locaux de denrées alimentaires exercent un plus grand pouvoir de marché, ce qui se traduit par des prix plus élevés. Nous vérifions ce résultat théorique à l’aide d’une expérience naturelle qui nous est offerte grâce à la plus grande concentration dans le temps des paiements d’aide sociale à Montréal (Québec, Canada) qu’à Bangor (Maine, USA). Nous trouvons que: i) Les prix des denrées diminuent de manière significative lors de la semaine pendant laquelle les paiements d’aide sociale sont faits; ii) Les prix

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)0.015
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.213
Threshold uncertainty score0.429

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0030.002
Scholarly communication0.0060.002
Open science0.0020.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0520.005

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 head0.128
GPT teacher head0.341
Teacher spread0.213 · 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 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
Published2002
Admission routes1
Has abstractyes

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