MétaCan
Menu
Back to cohort
Record W2096304226 · doi:10.55504/0884-9153.1201

Financial Planning for Postsecondary Education in Canada: A Comparison of Savings and Savings Instruments Employed Across Aspiration Groups

2004· article· en· W2096304226 on OpenAlexaffabout
Paul Anisef, Robert Sweet, Peggy M. L. Ng

Bibliographic record

VenueJournal of Student Financial Aid · 2004
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsLakehead UniversityYork University
Fundersnot available
KeywordsGovernment (linguistics)Higher educationPostsecondary educationState governmentBusinessEconomicsPsychologyEconomic growthPolitical scienceFinanceSociologyLocal governmentPublic administration

Abstract

fetched live from OpenAlex

Canadians have experienced a reduction in government funding toward postsecondary education over the past ten years, as well as a shift in student-support policies. Historically, paying the costs of postsecondary education in Canada has been a responsibility shared by the state, and students and parents. Changes in government policy have forced families to assume a greater share of their children’s postsecondary costs and have required a shift in their educational planning priorities. There is a corresponding need on the part of policy researchers to better understand this reorientation. The purpose of this paper is to document how parents save for higher education in relation to the educational aspirations they hold for their children (i.e., community college, trade school, or university). The analysis compares the effect of selected sociodemographic factors on parents’ savings and use of savings instruments across community college, trade school, and university aspiration categories.

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)0.003
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 score0.039
Threshold uncertainty score0.282

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.295
Teacher spread0.278 · 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

Citations1
Published2004
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Student Financial AidSame topicFinancial Literacy, Pension, Retirement AnalysisFrench-language works237,207