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Record W2044608169 · doi:10.1080/03075079.2014.899342

The iron law of hierarchy? Institutional differentiation in UK higher education

2014· article· en· W2044608169 on OpenAlexaboutno aff
Linda Croxford, David Raffe

Bibliographic record

VenueStudies in Higher Education · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational and Educational Inequality Studies
Canadian institutionsnot available
FundersUniversity of Chinese Academy of SciencesNuffield FoundationSociety for Research into Higher Education
KeywordsHierarchyHigher educationEthnic groupSociologySocial classQuarter (Canadian coin)Dimension (graph theory)Variation (astronomy)Subject (documents)Demographic economicsPolitical scienceGeographyLawEconomics

Abstract

fetched live from OpenAlex

This paper maps the main dimensions of differentiation among institutions and ‘faculties’ (subject areas within institutions) of higher education in the United Kingdom. It does so through a principal components analysis based on the characteristics of applicants and entrants. A single status dimension accounts for a quarter of the variation, and is associated with the social class, educational background, age (under 21) and non-local origin of students. This dimension is very stable over time and across England, Wales and Scotland. It is robust in the face of alternative specifications. The paper argues that this institutional hierarchy is deeply embedded in wider social structures and reflects the social reproduction role of higher education. Other, somewhat less stable dimensions, are associated with students' ethnic background and domicile.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.068
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0010.006
Scholarly communication0.0030.002
Open science0.0000.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.109
GPT teacher head0.412
Teacher spread0.303 · 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

Citations102
Published2014
Admission routes1
Has abstractyes

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