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Record W2121381659

Public and Private Welfare Activity in the United Kingdom, 1979 to 1999

2005· article· en· W2121381659 on OpenAlexaboutno aff
Rachel Smithies

Bibliographic record

VenueLondon School of Economics and Political Science Research Online (London School of Economics and Political Science) · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsnot available
FundersEconomic and Social Research CouncilLondon School of Economics and Political Science
KeywordsWelfareQuarter (Canadian coin)Social securityBusinessTypologyPublic sectorBalance (ability)Social WelfarePrivate sectorKingdomDemographic economicsPublic economicsEconomicsEconomic growthPolitical scienceEconomyGeographyMedicineMarket economyLaw
DOInot available

Abstract

fetched live from OpenAlex

This paper analyses the shifting balance between public sector and private sector welfare provision in the United Kingdom over the past two decades. Five sectors - education, health, personal social services, housing, and income maintenance and social security - are examined over three time points, 1979/80, 1995/96, and 1999/2000. Burchardt's (1997) typology is used to classify welfare activities according to who funds them, who provides them, and who decides on the provider and/or amount of service. It is found that shifts in the composition of welfare activity have been relatively small and gradual: around half of all welfare activity, dropping from 52 percent to 49 percent, is entirely public; around a quarter, rising from 24 percent to 29 percent, is entirely private; and the remainder involves a mixture of both sectors. Within the latter group, there was a notable increase over time in the contracting-out of public services, which rose from 6 percent to 10 percent of all welfare activity.

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)0.000
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score0.787

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.131
GPT teacher head0.413
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 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

Citations3
Published2005
Admission routes1
Has abstractyes

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