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Record W2095361156 · doi:10.1177/1091142106292774

Debt Aversion and the Demand for Loans for Postsecondary Education

2007· article· en· W2095361156 on OpenAlexaffabout
Catherine C. Eckel, Cathleen Johnson, Claude Montmarquette, Christian Rojas

Bibliographic record

VenuePublic Finance Review · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsUniversité de MontréalCenter for Interuniversity Research and Analysis on Organizations
Fundersnot available
KeywordsDebtSubsidyEconomicsInvestment (military)PaymentFinanceCashStudent debtMonetary economicsActuarial scienceBusinessPolitical science

Abstract

fetched live from OpenAlex

The authors report the results of an experiment designed to measure the impact of different forms of subsidies on the demand for postsecondary education financing among a sample of adults ages 18–55 in Canada. The experiment presents subjects with a series of choices involving trade-offs between cash payments and grants or loans earmarked for full or part-time education. In addition, the experiment includes experimental measures of time and risk preferences, and an extensive survey of experience and attitudes. This article focuses on the role of a person's attitudes toward debt (debt aversion) and experience with debt ( debt use) in the decision to take up subsidized loans for postsecondary education. Using survey measures, the authors find no evidence that debt aversion is an important barrier to investment in postsecondary education. In addition, subjects with experience carrying and managing debt are more willing than others to take on additional debt to finance postsecondary education.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.221
Threshold uncertainty score0.439

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.013
GPT teacher head0.257
Teacher spread0.243 · 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

Citations31
Published2007
Admission routes2
Has abstractyes

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