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Record W2278600918 · doi:10.5539/ies.v9n3p114

Mitigating Consumptive Behavior: The Analysis of Learning Experiences of Housewives

2016· article· en· W2278600918 on OpenAlexvenueno aff
Suparti Suparti

Bibliographic record

VenueInternational Education Studies · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy and Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsHousewifeFinancial literacyPsychologyWelfarePedagogySociologyBusinessFinanceEconomicsGender studies

Abstract

fetched live from OpenAlex

The purpose of this study is to investigate the determinant of consumptive behavior by analyzing learning experiences of housewives as members of Family Welfare Movement (PKK) in Malang, East Java Indonesia. Financial literacy is defined as personal knowledge and capability in financial management. Sample of this study was 123 housewives and retrieved using convenience sampling method. The data was collected by using questionnaires and analyzed by using path analysis. The results of this study show that financial literacy significantly affects consumptive behavior of housewife. It means that financial education has become an urgency to be held in formal education level. However socio-demographic factors (e.g. age, educational background, and working experiences) are not correlated with consumptive behavior of housewife. Therefore, financial literacy is the determinant of consumptive behavior of housewife. Thus, as learning experiences proxies, financial literacy and socio-demographic factors seem to be complement.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.047
GPT teacher head0.353
Teacher spread0.306 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2016
Admission routes1
Has abstractyes

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