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Record W2594500139 · doi:10.1177/138826270400600402

Articles Originally Presented at the Eiss Conference on <i>Federalism and Subsidiarity in Social Security</i> in Rome, September 2004: Old-Age Benefits and Decentralisation: The Spanish Case in Comparative Perspective

2004· article· en· W2594500139 on OpenAlexaboutno aff
Jesús Ruiz-Huerta Carbonell, José Manuel Díaz Pulido

Bibliographic record

VenueEuropean Journal of Social Security · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicLocal Government Finance and Decentralization
Canadian institutionsnot available
Fundersnot available
KeywordsDecentralizationSubsidiarityFederalismEquity (law)Social securityPublic administrationFiscal federalismPolitical scienceIntergenerational equityEconomicsEuropean unionLawEconomic policy

Abstract

fetched live from OpenAlex

The decentralisation of social security in general, and old-age pensions in particular, has been proposed by several nationalist parties in Spain. The purpose of this article is to analyse the rationality of this proposal, taking into account efficiency and equity issues. It is divided into four sections. The first introductory section reviews the most important literature on fiscal federalism, applying it to pensions. The second section analyses the division of competencies in Spanish old-age benefits, focusing specifically on legal aspects. The third describes the degree of decentralisation of public transfers for the elderly in several countries: Belgium, Canada, Germany, the United States and the United Kingdom. The final section evaluates the current Spanish system, considers the possibility of decentralisation in the light of economic efficiency, territorial equity and lessons learnt from other federal countries, and examines the evolution of the principal quantitative variables regarding the old-age pensions in the different Autonomous Regions.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.750
Threshold uncertainty score0.923

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.046
GPT teacher head0.309
Teacher spread0.263 · 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 teacher head, 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

Citations1
Published2004
Admission routes1
Has abstractyes

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