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Record W7038799848

2021-4 Innis Lecture: Return on Student Loans in Canada

2021· article· en· W7038799848 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueScholarship@Western (Western University) · 2021
Typearticle
Languageen
FieldEngineering
TopicElectricity Theft Detection Techniques
Canadian institutionsBrock University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLoanVariation (astronomy)Ex-anteRate of returnStudent loanField (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

This paper uses new administrative data with detailed borrower information and lengthy repayment histories from the Canada Student Loans Program (CSLP) to measure rates of return on undergraduate student loans. We document substantial heterogeneity in returns based on information available at the time loans were disbursed, including province of residence, field of study, and institution of attendance. Field of study is a particularly important determinant of rates of return, explaining 22% of the variation in predicted returns across borrowers. We explore the implications of this variation for CSLP cross-subsidization across borrowers and potential risk-based loan limits. Given the variation in ex ante predicted returns across borrowers, using all available information at the time of loan disbursement, we study the implications of potential cream-skimming of high-return borrowers by private lenders.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.903
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.030
GPT teacher head0.260
Teacher spread0.230 · 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