MétaCan
Menu
Back to cohort
Record W2766533877 · doi:10.1177/0038040717739613

The Worldwide Growth of Private Higher Education: Cross-national Patterns of Higher Education Institution Foundings by Sector

2017· article· en· W2766533877 on OpenAlexaff
Elizabeth Buckner

Bibliographic record

VenueSociology of Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Educational Reforms and Inequalities
Canadian institutionsUniversity of Toronto
FundersDivision of Social and Economic SciencesWorld Bank GroupSpencer Foundation
KeywordsHigher educationInstitutionNormativePrivate sectorPolitical scienceEconomic growthPublic institutionSociology of EducationSociologyPublic administrationEconomicsSocial scienceLaw

Abstract

fetched live from OpenAlex

This article investigates cross-national patterns of public and private higher education institution (HEI) foundings from 1960 to 2006. It argues that in addition to national demographic and economic factors, patterns of HEI foundings also reflect world-level models about how nations should structure their higher education systems. Findings document a rapid, recent rise in new private HEIs and point to supranational normative, mimetic, and coercive pressures that have encouraged nations to expand private higher education, including international development aid trends in peer nations, and linkages to intergovernmental organizations. I argue that while the public-sector HEI has been a long-standing and globally legitimated model for national development, private higher education has historically been associated with some world regions but not others. However, over the past two decades, supranational actors and ideas helped legitimate the private HEI as an acceptable model, spreading it even in regions that previously eschewed private higher education.

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)0.011
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0070.010
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.046
GPT teacher head0.401
Teacher spread0.355 · 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

Citations87
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueSociology of EducationSame topicGlobal Educational Reforms and InequalitiesFrench-language works237,207