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Record W7097475376

Boikos and the participants in the Jeudi-midi Seminar at the Université Laval, Département d’économique; as well

2015· article· en· W7097475376 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsInequalityGovernment (linguistics)Relevance (law)Fiscal policyEconomic inequalityWork (physics)Government spending
DOInot available

Abstract

fetched live from OpenAlex

Assessing the progressivity of a fiscal system is relevant to develop a global idea on the extent of redistribution. In this paper we assess the evolution of progressivity over time and how economic shocks and government fiscal policy affects its design. The social performance of fiscal redistributive mechanisms in Mexico has been receiving a growing interest from politicians and researchers. The aim of this paper is to assess the dynamics of progressivity of the fiscal system in Mexico and its effect on inequality and on polarization, and this during the period of 2002-2012. What distinguishes this work is the relevance of the adopted comparison approach of progressivity and where the common support of comparison is imposed. The results of this study confirm the effectiveness of the governmental redistributive mechanisms to decrease after-tax income inequality. Based on our estimates, we find a significant increase in the progressivity of the fiscal system over time, despite the high persistent levels of

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)0.005
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score0.223

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0670.010

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 head0.074
GPT teacher head0.243
Teacher spread0.169 · 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.

Study designNot applicable
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

Citations0
Published2015
Admission routes1
Has abstractyes

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