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Record W2896347717 · doi:10.1093/ser/mwy038

Is social investment inimical to the poor?

2018· article· en· W2896347717 on OpenAlexafffund
Alain Noël

Bibliographic record

VenueSocio-Economic Review · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsInvestment (military)EconomicsWelfare stateWelfarePoliticsSocial protectionSocial WelfareState (computer science)Public economicsLabour economicsEconomic policyMarket economyEconomic growthPolitical science

Abstract

fetched live from OpenAlex

Abstract In the last two decades, the social investment strategy has been the main approach to welfare state reform. Concretely, two spending programs have dominated the agenda: the expansion of active labor market programs and the development of childcare services. Many authors have suspected, however, that these social investments were realized at the expense of income protection for the poor. This article assesses this potential trade-off with time-series cross-sectional models of the determinants of active labor market policies expenditures, childcare spending and the adequacy of minimum income protection (MIP), for 18 OECD countries between 1990 and 2009. It turns out that social investments are rather akin to traditional welfare state programs, and are explained by similar institutional, political and economic factors. More importantly, they do not develop at the expense of income protection. Social investment initiatives are consistent with the usual politics of the welfare state and, overall, they are not inimical to the poor.

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)0.004
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.003
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.094
GPT teacher head0.413
Teacher spread0.319 · 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

Citations48
Published2018
Admission routes2
Has abstractyes

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