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Record W4416289032 · doi:10.38116/td3160-eng

Discussion Paper 3160

2025· book· en· W4416289032 on OpenAlexaboutno aff
Marcelo de Sales (Colaborador) Pessoa

Bibliographic record

VenueInstituto de Pesquisa Econômica Aplicada eBooks · 2025
Typebook
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPensionSustainabilityPopulation ageingFiscal sustainabilityPrivate pensionPopulationPublic policy

Abstract

fetched live from OpenAlex

This paper provides a comparative analysis of the pension systems in Brazil and Canada, focusing on their historical evolution, structural frameworks, and the demographic and fiscal challenges they face. By examining the development of these systems, the study highlights the unsustainable trajectory of Brazil’s public pension system, which is projected to consume 13.9% of gross domestic product (GDP) by 2060 due to population aging and declining fertility rates. Drawing on lessons from Canada’s balanced three-pillar system – comprising universal public benefits, mandatory contributory plans, and voluntary private savings – the paper proposes a series of policy recommendations for Brazil. These include expanding access to private pension plans, promoting individual retirement savings, reforming the public pension system, and leveraging immigration to mitigate demographic pressures. The findings underscore the importance of increasing the role of private retirement savings in Brazil to ensure long-term fiscal sustainability and retirement security, while also identifying areas for future research to support ongoing reform efforts.

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)0.000
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 score0.449
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2025
Admission routes1
Has abstractyes

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