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Record W4322770761 · doi:10.3390/jrfm16030165

Challenges in Understanding Western Economic and Financial Concepts from the Perspective of Young Adults with a Post-Soviet Migration Background in Germany—Findings from a Qualitative Interview Study

2023· article· en· W4322770761 on OpenAlexvenueno aff
Sebastian Heidel, Roland Happ

Bibliographic record

VenueJournal of risk and financial management · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
FundersJoachim Herz Stiftung
KeywordsSocializationFinancial literacyPerspective (graphical)Qualitative researchEuropean unionSoviet unionPolitical scienceSociologyEconomic growthEconomicsSocial scienceFinanceEconomic policyPolitics

Abstract

fetched live from OpenAlex

The content of economic education in Germany is based largely on the laws and ideals of the prevailing economic system. While Western concepts such as the competitive market typically are addressed in economic programs in Germany, they may be unfamiliar in Eastern European countries that were part of, or under the influence of, the former Soviet Union, where many youths living in Germany originate. Findings from large-scale quantitative studies of economic and financial literacy in Germany indicate that people who have a migration background (MB) perform worse on tests of economic literacy than those who do not; however, these studies do not provide sufficient insight into the underlying migration-related causes of the deficits in economic literacy. In this study we investigate the influence of family financial socialization on young adults’ understanding of economic and financial concepts. We interview eight young adults with a post-Soviet MB living in Germany using a two-part procedure: problem-centered and think-aloud interviews. We found that migrant parents directly and indirectly influenced their children’s understanding of economic and financial concepts in numerous ways, and we maintain that the best way to remedy the deficits in their understanding of such concepts is through targeted programs and teacher training.

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 score0.121
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.049
GPT teacher head0.295
Teacher spread0.246 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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