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Record W1782343898 · doi:10.1111/caje.12137

The lack of loan aversion among Canadian high school students

2015· article· en· W1782343898 on OpenAlexafffundvenueabout
Cathleen Johnson, Claude Montmarquette

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsUniversité de MontréalCenter for Interuniversity Research and Analysis on Organizations
FundersCanada Millennium Scholarship Foundation
KeywordsLoanSubsidySample (material)NumeracyCashFinanceFinancial literacyBusinessEconomicsDemographic economicsEconomic growthLiteracy

Abstract

fetched live from OpenAlex

Abstract Evidence is presented on the factors that influence take‐up for postsecondary education financing (loans or grants) for high school students. Results show several factors influence the students' decisions about taking loans or grants but the most prominent influence was the price of educational subsidy. A total of 1,248 high school students across Canada participated. Prices for the grants and loans overlapped substantially in order to more clearly distinguish the impact of loan aversion on the decision to take up financial assistance to pursue PSE. The study featured paid experimental decisions (ranging from $25 to $700 in cash and from $500 to $4,000 in education financing), a numeracy assessment, a student survey and a parental telephone survey. The targeted sample included at‐risk high school students: low SES levels, First Nations and first generation students. Participants were marginally sensitive to the form of financing (grant or loan), with no evidence of systematic loan aversion being detected.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.108
GPT teacher head0.201
Teacher spread0.093 · 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 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

Citations8
Published2015
Admission routes4
Has abstractyes

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