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Lessons from Pension Reform in the Americas

2007· book· en· W2989222735 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typebook
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsLatin AmericansPensionGovernment (linguistics)Western hemisphereNobel laureatePolitical scienceDevelopment economicsEconomic growthEconomicsLawInternational trade

Abstract

fetched live from OpenAlex

Abstract Latin American experiments with pension reform began when Chile converted its public pay-as-you-go system to a system of private individual accounts in the early 1980s. Several other Latin American countries then followed suit, inspired both by Chile's reforms and by World Bank recommendations stressing compulsory government-mandated individual saving accounts. Individual accounts were subsequently introduced in a number of countries in Europe and Asia. Many are now re-evaluating these privatizations in an effort to ‘reform the reform’ to make these systems more efficient and equitable. This book assesses pension reforms in this new ‘post-privatization’ era. After a discussion on demographic trends in the foreword by Nobel laureate Robert W. Fogel, Section 1 of the book includes chapters on the role of pension system default options, the impact of gender, and a discussion of the World Bank's policies on pension reform. The chapter on the evidence from Chile's new social protection survey points to key lessons from the world's first privatization. Section 2 offers analysis of several significant reform initiatives in the hemisphere, and includes chapters on the United States, Canada, Mexico, Costa Rica, Brazil, Peru, Uruguay, and Argentina.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.305
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.033
GPT teacher head0.276
Teacher spread0.242 · 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