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Record W2131330528 · doi:10.47678/cjhe.v34i1.183447

The Tuition Dilemma and the Politics of "Mass" Higher Education

2004· article· en· W2131330528 on OpenAlexaffvenueabout
Richard Wellen

Bibliographic record

VenueCanadian Journal of Higher Education · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsYork University
Fundersnot available
KeywordsDilemmaSubsidyWarrantHigher educationPoliticsCritical mass (sociodynamics)Diversity (politics)EconomicsRelevance (law)SociologyPublic economicsPolitical sciencePublic administrationEconomic growthPolitical economySocial scienceMarket economyLawFinance

Abstract

fetched live from OpenAlex

The prospect of tuition fee increases for public sector universities has attracted an enormous amount of attention in recent years as governments in all industrialized countries have responded to the converging pressures of increased demands for higher education and rising costs of competing areas of social spending. I show that this dilemma is fast approaching a critical point in both Canada and the UK. As contemporary society become "knowledge societies," postsecondary systems become "complex," requiring a sensitive political blending of different institutional goals, such as accessibility, diversity of mission, critical thought, relevance, and social usefulness. This article draws upon the policy model of income contingent repayment (ICR) as a touchstone for debates and larger proposals about addressing the future of higher education reform. My hope is to show the partial shortcomings of the traditional alternatives: reliance on state-provided subsidy on the one hand and deregulated and flexible fees on the other. I then argue that changes in the social and political meaning of participation in higher education might warrant taking a second look at the "smart funding" approach represented by ICR proposals.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.290
Teacher spread0.277 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations7
Published2004
Admission routes3
Has abstractyes

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