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Record W4385212617 · doi:10.3390/jrfm16070342

Differences in the Destination of Savings According to Gender, and Its Economic Rights Implications

2023· article· en· W4385212617 on OpenAlexvenueno aff
Florina Guadalupe Arredondo Trapero, Eva María Guerra-Leal, José Carlos Vázquez Parra

Bibliographic record

VenueJournal of risk and financial management · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
FundersInstituto Tecnológico y de Estudios Superiores de Monterrey
KeywordsDestinationsReal estateTest (biology)Demographic economicsContrast (vision)EconomicsPolitical scienceFinanceTourism

Abstract

fetched live from OpenAlex

The main problem this article addresses is that women are more vulnerable than men in economic terms. The aim of this research is to identify the differences in the destination of savings according to gender and its implications related to their economic rights. Chi-Square tests were performed to test for the existence of statistically significant differences in the destination of men’s and women’s savings, based on the National Survey on the Destination of Savings in Mexico (ENIF). The hypothesis to be tested is that there is a gender difference in the way in which the destination of savings is allocated. As a result, it is possible to see that women focus their savings on issues related to health and education at home, in contrast to men, who tend to protect their own economic future by focusing their savings on remodeling or buying real estate or starting or expanding a business. In this sense, the hypothesis is partially verified in 4 of the 8 savings destinations. This article is motivated by the desire to identify this possible economic gap between genders, considering that it is an issue that affects the economic and personal future of women.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.310

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.022
GPT teacher head0.239
Teacher spread0.216 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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