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Record W4307550132 · doi:10.3390/jrfm15110494

An Uphill Battle: Financial Education in Romania in the Midst of Societal Transformation

2022· article· en· W4307550132 on OpenAlexvenueno aff
Radu Șimandan, Beatrice Leuștean, Răzvan Mihai Dobrescu

Bibliographic record

VenueJournal of risk and financial management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFinancial literacyConsumerismPublic relationsPolitical scienceLiteracyEconomic growthSociologyBusinessEconomicsFinance

Abstract

fetched live from OpenAlex

Given Romania’s relatively poor performance from a financial literacy perspective, many public and private entities are currently implementing various initiatives to address this problem. Assuming that financial education projects are a source of insights into broader societal issues, we analyze a sample of financial education projects to discern the issues of societal transformation reflected in their contents. We collected data from financial education websites and analyzed them through qualitative content analysis. We identify and discuss several manifest and latent themes and note the absence of others commonly found in the literature. The emphasis in the manifest themes falls on offering calibrated advice to a public with a relatively low level of financial literacy, prone to unhealthy behaviors such as consumerism, impulse buying, and indebtedness. Several latent concerns concentrate on the changing economic and social landscape, the fear that some traits of national character may hinder the individual appetite for adaptation, and the threat of an economic crisis. The needs of vulnerable groups are rarely addressed, while topics such as the ethical dimension of consumption, environmental and sustainability issues, and gender stereotypes are lacking. We thus find that the financial education initiatives in Romania address an underdeveloped range of topics.

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)0.002
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 score0.176
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.005
GPT teacher head0.218
Teacher spread0.213 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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