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Record W2047162418 · doi:10.1177/016059760903300108

Social Inequalities, Public Policy, and Health

2009· article· en· W2047162418 on OpenAlexaff
Toba Bryant

Bibliographic record

VenueHumanity & Society · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWelfare stateSocial policyInequalityPovertySocial securityPublic policyPublic healthHealth policyEconomic growthSocial WelfareSocial inequalityPoliticsGovernment (linguistics)Social determinants of healthState (computer science)Health careIdeologyWelfarePolitical scienceEconomicsMedicine

Abstract

fetched live from OpenAlex

Social inequalities are a key issue for modern welfare states in the post-industrial globalized economy. The extent of social inequalities within nations is directly related to the size and nature of the welfare state. The welfare state was the mechanism by which governments in developed economies intervened in health and social public policy areas to ensure access to health care and social services. Public policy has a significant impact on social and health inequalities. The specific public policies that influence these are government provision of income and housing security, program support for individuals and families, and poverty reduction. Much of this involves governmental transfers of national wealth among the citizenry. This paper presents and discusses some of the key differences among advanced nations in terms of governmental transfers in broad policy areas as well as the specific areas of health care, old age, incapacity, and families. These issues tie into the political ideology of governments, the nature of the political and electoral systems of countries, and how public policies can reduce inequalities between different groups.

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)0.007
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.013
Scholarly communication0.0050.003
Open science0.0000.003
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0070.000

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 head0.211
GPT teacher head0.435
Teacher spread0.223 · 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
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

Citations6
Published2009
Admission routes1
Has abstractyes

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