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The impact of cost on the choice of university: evidence from Ontario

2012· article· en· W1604267261 on OpenAlexaffvenueabout
Martin Dooley, A. Abigail Payne, A. Leslie Robb

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsScholarshipWelfareDemographic economicsLiberal arts educationEconomicsValue (mathematics)Net incomeThe artsHigher educationActuarial sciencePublic economicsLabour economicsSociologyEconomic growthPolitical scienceMathematicsLawFinance

Abstract

fetched live from OpenAlex

Abstract. This paper provides the first Canadian study of the link between cost to the student and the choice of university. Over the past two decades, there has been a substantial increase in the differences among Ontario universities in ‘net cost’ defined as tuition and fees minus the expected value to an academically strong student of a guaranteed merit scholarship. Our estimates generally indicate no relationship between net cost and the overall share of strong applicants that a university is able to attract. An increase in net cost is associated with an increase in the ratio of strong students from high‐income neighbourhoods to strong students from middle‐income and low‐income neighbourhoods in Arts and Science programs but not in Commerce and Engineering. Finally, more advantaged students are more likely to attend university, but merit aid is not of disproportionate benefit to those from more economically advantaged backgrounds, given registration. JEL classification: Health Education and Welfare

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)0.015
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 score0.959
Threshold uncertainty score0.297

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.006
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.268
GPT teacher head0.288
Teacher spread0.021 · 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

Citations13
Published2012
Admission routes3
Has abstractyes

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