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Record W7117718986 · doi:10.47678/cjhe.v55i4.191173

Book review of "Capitalizing on College: How Higher Education Went from Mission Driven to Margin Obsessed"

2025· article· en· W7117718986 on OpenAlexaffvenueabout
Daniel Lang

Bibliographic record

VenueCanadian Journal of Higher Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMargin (machine learning)Higher education

Abstract

fetched live from OpenAlex

In some respects -in fact, most --Capitalizing on College: How Higher Education Went from Mission Driven to Margin Obsessed says little of relevance to higher education in Canada.But in those few respects that do have a bearing on the Canadian experience, the book can be a thought-provoking read.First, it is important to understand that the book's title is an over-statement.It is not about higher education at large.It is about higher education in the United States and, more particularly, about a single sector: religious colleges.Religious colleges constitute a large sector of higher education in the United Status, even larger when measured in terms of undergraduate education.By that definition there are only few Canadian counterparts for example, Redeemer, Tyndale, and Trinity-Western, all sectarian institutions offering secular degrees.Professor Brown's explanation of this focus is that a broader focus would have been methodologically unmanageable.There is practical truth to that: the book's research relies entirely on 150 confidential in-person interviews at eight colleges, each identified by a pseudonym.These are in turn organized into four "strategies" that were adopted from previous studies of for-profit colleges, an interesting methodological choice that deserves explanation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.545
Threshold uncertainty score0.995

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.326
Teacher spread0.309 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2025
Admission routes3
Has abstractyes

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