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Record W2119699839 · doi:10.1177/1468018114539692

Mexico and social provision by the federal government and the federal district: Obstacles and openings to a Social Protection Floor

2014· article· en· W2119699839 on OpenAlexafffund
Lucy Luccisano, Laura Macdonald

Bibliographic record

VenueGlobal Social Policy · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicPolitics and Society in Latin America
Canadian institutionsCarleton UniversityWilfrid Laurier University
FundersSocial Sciences and Humanities Research Council of CanadaWilfrid Laurier UniversityUniversity of WaterlooInter-American Development Bank
KeywordsPovertyPolitical scienceSocial protectionPublic administrationEconomic growthSocial policyGovernment (linguistics)Social rightsEconomicsPoliticsLaw

Abstract

fetched live from OpenAlex

This article examines the lessons that can be drawn from social protection programs in Mexico at both the national and sub-national scales for the Social Protection Floor (SPF) initiative for the implementation of the Global Social Floor proposal in Mexico. Mexico’s federal anti-poverty program, Progresa/Oportunidades, was a pioneer in the application of a social investment paradigm to the provision of social benefits to the extreme poor, and the targeting of benefits. At the same time, the left-leaning Partido de la Revolución Democrática (PRD) governments of the Federal District have introduced an ambitious new series of social programs at the municipal scale. PRD governments have directly criticized the targeting and surveillance involved in the Oportunidades program. Instead, the PRD has advocated more universalistic approaches to social policy, based on principles of social rights. This article examines the areas of congruence and dissonance between these Mexican innovations in social policy and the SPF initiative.

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)0.006
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.136
Threshold uncertainty score0.270

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.009
Scholarly communication0.0100.004
Open science0.0010.007
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0060.000

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 head0.011
GPT teacher head0.294
Teacher spread0.282 · 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 designQualitative
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

Citations17
Published2014
Admission routes2
Has abstractyes

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