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Record W3017263980 · doi:10.1017/s1474746420000172

Examining Attitudes towards Welfare in an In/Security Regime: Evidence from Ghana

2020· article· en· W3017263980 on OpenAlexaff
Padmore Adusei Amoah

Bibliographic record

VenueSocial Policy and Society · 2020
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsDistrustWelfareGovernment (linguistics)PoliticsPreferencePublic opinionWelfare statePublic economicsPolitical scienceSocial securityPublic policyDevelopment economicsEconomicsEconomic growthBusinessMarket economy

Abstract

fetched live from OpenAlex

This article examines the extent to which Gough and Woodʼs (2004) classification of most sub-Saharan African nations as insecurity regimes is still relevant by analysing public responses and attitudes towards general and specific (healthcare) welfare policies in Ghana, using a mixed-method design. Ghana presents a fascinating case study not only due to the changing socio-economic landscape but also because of the prevailing socio-political stability. The research findings demonstrate that most participants wanted more welfare spending (including on healthcare) but remained reluctant to rely on government provisions due to distrust and perceived inefficiencies in the public sector. The findings also depict the continuing reliance on family and social networks as safety nets and sometimes in preference to state arrangements. The article argues that Ghanaʼs welfare regime may be gradually shifting from the classic insecurity regime (albeit still relevant) to one resembling the less effective informal security regime – at least from the publicʼs experiences – and demands a careful integration of individual, familial, and community networks in current and future formal welfare arrangements.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.315
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.196
GPT teacher head0.458
Teacher spread0.261 · 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 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

Citations18
Published2020
Admission routes1
Has abstractyes

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