MétaCan
Menu
Back to cohort
Record W4244210586 · doi:10.4324/9780203876664-7

Higher Education: An Emerging Field of Research and Policy

2010· book-chapter· en· W4244210586 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
Fundersnot available
KeywordsField (mathematics)Political scienceMathematics

Abstract

fetched live from OpenAlex

An Emerging Field of Research and Policy PHILIP G. ALTBACHHigher education has become a vast twenty-first century enterprise, central to postindustrial globalized economies everywhere. More than 100 million students study in at least 36,000 postsecondary institutions worldwide. In most countries, higher education has become a large, complex enterprise, comprising large academic systems, nonprofit and for-profit private institutions, and an array of specialized schools. As universities and other postsecondary institutions have grown, they acquire elaborate administrative structures in need of major expenditures of public and, often, private funds. Moreover, higher education has become big business. Academic institutions employ thousands of people and educate tens of thousands-or in some cases hundreds of thousands. Degrees in a multiplicity of specialties from ancient history to biotechnology are offered. In 1971 Eric Ashby characterized the American academic system as offering “any person, any study,” in describing its diversity and scope. Martin Trow analyzed the progression of higher education from elite to mass and finally to universal access (2006). In the industrialized nations, at least, mass access has been achieved, and a few countries-first the United States and Canada and, recently, South Korea, Finland, Japan, and many others-enroll upwards of 70 percent of the relevant age group. Many others, mainly in Europe and the Pacific Rim, educate half or more of the age group. Developing countries lag behind, and the main growth in the coming decades will be in this part of the world (World Bank 2000).

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.834
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.000

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 head0.085
GPT teacher head0.469
Teacher spread0.384 · 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 designTheoretical or conceptual
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

Citations13
Published2010
Admission routes1
Has abstractyes

Explore more

Same topicHigher Education Governance and DevelopmentFrench-language works237,207