MétaCan
Menu
Back to cohort
Record W2807926272 · doi:10.5539/ibr.v11n7p120

Identification of Barriers to Financial Inclusion Among Youth

2018· article· en· W2807926272 on OpenAlexvenueno aff
Amra Babajić, Jasmina Okičić, Meldina Kokorović Jukan

Bibliographic record

VenueInternational Business Research · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
FundersFederalno Ministarstvo Obrazovanja i Nauke
KeywordsFinancial inclusionUnbankedDebit cardCredit cardFinancial servicesFinancial institutionIdentification (biology)Bank accountBusinessInclusion (mineral)InstitutionFinancial literacyIndependence (probability theory)Financial independenceFinancial systemFinancePaymentEconomicsAccountingPolitical sciencePsychology

Abstract

fetched live from OpenAlex

In recent years there is an increasing research attention on youth and their transition to adulthood. In that transition they have increasing demand for financial products and services. If they are not financial included it may leave long-lasting consequences for their future independence and stability.The main goal of this research is to investigate and explain barriers to poor financial inclusion of youth in Federation of Bosnia and Herzegovina (FBiH), and make some recommendations for increasing their financial inclusion, and indirectly for strengthening their social inclusion. Implications of this study suggest that the main reason for being unbanked is because someone else in the family already has an account, or because they do not have enough money to use services of financial institutions. The results have revealed statistically significant relation between need for financial services at a formal institution and having a bank account, category of students’ financial knowledge and having a bank account, having a debit card and having a credit card. Research results can serve the economic and social policy makers in the FBiH in policy and strategy design.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.034
GPT teacher head0.328
Teacher spread0.294 · 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.

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

Citations5
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueInternational Business ResearchSame topicFinancial Literacy, Pension, Retirement AnalysisFrench-language works237,207