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Record W2100207899 · doi:10.2202/1944-2858.1018

Ideas, Institutions, and Welfare Program Typologies: An Analysis of Pensions and Old Age Income Protection Policies in Sub‐Saharan Africa

2010· article· en· W2100207899 on OpenAlexaff
Michael Kpessa-Whyte

Bibliographic record

VenuePoverty & Public Policy · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAusterityWelfareSocial policySocial securityWelfare statePensionEconomicsNeglectOld Age SecurityDevelopment economicsSocial protectionEconomic growthPolitical scienceSociologyPoliticsMarket economyPopulationLaw

Abstract

fetched live from OpenAlex

Abstract Since the 1980s, social policy research shifted attention from institutional development of welfare programs to what were described as crises of the welfare state in an era of austerity. Much of the scholarly debate in this area had focused on the maturation of welfare programs, especially the post‐war old age income support programs in the advanced industrialized countries to the neglect of social protection in Sub‐Saharan African (SSA) countries. This paper is intended to bring the dynamics of social policy in SSA countries into the comparative welfare dialogue and into the global social security debate in particular. Using a historical institutionalist approach, this study analyzes the trajectories of old age income support development in SSA countries through a careful study of old age income security or protection strategies in the region across time and space. The paper develops ideal typologies for understandings variations and transformations of pensions and old age income provision programs in the region. In doing this, it argues that the ideas and institutions around which recent rounds of pension reforms revolves have always been at both the foreground and background of old age income protection thinking and practices in SSA countries since the pre‐colonial era.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.855
Threshold uncertainty score0.813

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.061
GPT teacher head0.363
Teacher spread0.303 · 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

Citations34
Published2010
Admission routes1
Has abstractyes

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