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Record W2904980609 · doi:10.5430/afr.v8n1p30

Financial Literacy and Behavioral Biases among Traditional Age College Students

2018· article· en· W2904980609 on OpenAlexvenueno aff
Ohannes George Paskelian, Kevin Jones, Stephen Bell, Robert Kao

Bibliographic record

VenueAccounting and Finance Research · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFinancial literacyDistrustRetirement planningFinancial planFinanceLiteracyPerceptionPsychologyBusinessActuarial sciencePedagogy

Abstract

fetched live from OpenAlex

Financial literacy and planning are crucial for everyone. This is especially true for college students who as the decisions they make in this stage of their lives can haunt them throughout their income earning years and beyond. In this paper, we examine several financial literacy issues facing college students. We identify college students’ perceptions about their own financial situation, assess student financial literacy knowledge, as well as evaluate their awareness about the status of their savings and retirement positions. We find that basic financial literacy is not the only factor in making sound financial decisions. Our results show the majority of the college students surveyed are financially literate and have the ability to make informed decisions about their personal finances in the short-run. While our respondents appear confident in making short-run financial decisions, their behavior tends to suggest that their confidence is somewhat misguided. In addition, a large number of the students surveyed feel they do not have the requisite knowledge to make wise retirement planning choices. Furthermore, several respondents report a distrust of retirement plans offer by private companies, which may lead to suboptimal retirement savings.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0000.001
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 head0.093
GPT teacher head0.368
Teacher spread0.275 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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