MétaCan
Menu
Back to cohort
Record W2802060896

Financial literacy education with Aboriginal people: The importance of culture and context

2016· article· en· W2802060896 on OpenAlexaboutno aff
Levon Blue

Bibliographic record

VenueQUT ePrints (Queensland University of Technology) · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFinancial literacyContext (archaeology)PraxisPublic relationsLiteracySociologyQualitative researchFinancial servicesPolitical sciencePedagogyBusinessFinanceSocial science
DOInot available

Abstract

fetched live from OpenAlex

Learning about effective ways to manage money is a 21st century skill and the focus of financial literacy education (FLE) initiatives globally. Individuals deemed to have lower levels of financial literacy are often the targets of financial literacy workshops/programs. In this research paper the key outcomes are identified from a qualitative study that explored the FLE practices with an Aboriginal community in Canada (that the author is a member of). This includes conceptualising what influences financial decision-making and the role of culture and context in financial literacy education with a Community instead of for a Community. Discussed is the importance of adopting a praxis approach to FLE and integrating site based education development with Community members instead of imposing education on individuals. Last, outlined in this paper is how the above findings may offer insights for financial educators and/or planners participating in financial literacy education and engaging with Aboriginal clients.

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)0.003
metaresearch head score (Gemma)0.006
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.389

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.005
Scholarly communication0.0040.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.003
GPT teacher head0.195
Teacher spread0.192 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueQUT ePrints (Queensland University of Technology)Same topicFinancial Literacy, Pension, Retirement AnalysisFrench-language works237,207