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Record W2998838225 · doi:10.5430/ijba.v11n1p1

An Analysis on Social Security in Brazil Based on Maranhão

2020· article· en· W2998838225 on OpenAlexvenueno aff
Fernando Silva Lima, Alessandra Silva Pires, Francisco de Assis Pereira Filho, Michelle Matilde Semiguem Lima Trombini

Bibliographic record

VenueInternational Journal of Business Administration · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInsurance and Financial Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsSocial securityPensionChamber of DeputiesProfit (economics)EconomicsDemographic economicsBusinessFinancePolitical scienceLegislatureMarket economyLaw

Abstract

fetched live from OpenAlex

This study begins with the question: can the new change in the old-age pension system improve the economic-financial performance of the National Institute of Social Security in Brazil? The hypothesis is that the proposed constitutional amendment (PEC) 287/16, which is being presented at the Chamber of Deputies known as the pension reform, including an attempt to change the minimum age for men and women, will not solve the problem of economic crisis and financial expenses of the National Institute of Social Security of Brazil, due to the fact that the greatest impact may be other expenses not identified in this study that revolve around the benefits of retirement. The general objective is to analyze the economic and financial situation of social security in Brazil based on the regional accounting records located in Imperatriz and São Luís do Maranhão between 2008 and 2017. The methodology was defined as descriptive, explanatory and average, such as bibliofigurey, documentary and field. One of the results regarding the increase in the retirement age shows that there is no relation between the income increase indicators when compared to the surplus (profit) or deficit (loss) between 2008 and 2017 in the Social Security of Maranhão.

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 score0.480
Threshold uncertainty score0.439

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.030
GPT teacher head0.281
Teacher spread0.251 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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