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Record W2795851746 · doi:10.20448/2002.22.55.63

Financial Education for Child and Youth Care Practitioners

2018· article· en· W2795851746 on OpenAlexaff
G. Lechner, Ninnia Craß, Dashenka Kraleva, Veronika Scharer, Jakub Iwański, S. Giraud

Bibliographic record

VenueJournal of Accounting Business and Finance Research · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Education Studies and Reforms
Canadian institutionsCentre Jeunesse de Quebec
FundersErasmus+European Commission
KeywordsFocus groupFocus (optics)FinanceMeaning (existential)BusinessPsychologyMedical educationPublic relationsSociologyPolitical scienceMarketingMedicine

Abstract

fetched live from OpenAlex

This paper aims to identify the needs of financial education of Child and Youth Care practitioners (CYCPs). There is not much knowledge about the deficits of financial education of young people in care (YPC) and young care leavers (YCL) and we want to bring more light into this difficult topic. We use focus group interviews in five different countries (Bulgaria, Austria, Germany, Poland, and France) to find the needs of the CYCPs with a special focus on the needs of YCLs. Our focus groups showed that CYCPs do not have enough basic and further education to handle the situation with YPC and YCLs successfully. We found that the needs of the YPC and YCLs concerning financial education are consistent with the needed knowledge, skills and competences of the CYCPs. Some needs of CYCPs and young people in care are to understand the meaning of money, to handle pocket money, to understand the logic of credit and financial contracts and to know how an online bank account works.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.052
GPT teacher head0.401
Teacher spread0.349 · 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 designNot applicable
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

Citations4
Published2018
Admission routes1
Has abstractyes

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