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Record W2901944630 · doi:10.7202/1053582ar

Paper Bastions: Architecting Academic Citadels from 1991 to 2009

2018· article· en· W2901944630 on OpenAlexvenueno aff
Stephen J. Walsh, Jie Yan, Vincent Mangematin, Maggie Qiuzhu Mei

Bibliographic record

VenueManagement international · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEliteDominance (genetics)LeaguePolitical sciencePublic relationsSociologyLawBiology

Abstract

fetched live from OpenAlex

How were paper bastions added to the walls of academic citadels? By mapping the evolution of the coauthorship network in 180 management journals from 1991 to 2009, we identify an elite league of business schools that retained dominance despite the research community’s significant growth. The elite universities maintain their prominence through a loop of reinforcement involving the peer review process and third-party ranking bodies, though the perceived quality of the papers published was declining as measured by the percentage of overall citations. Leading U.S. universities dominate top journal publications, while new local poles of management research among European and Asian universiteis emerged.

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)0.043
metaresearch head score (Gemma)0.166
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.977
Threshold uncertainty score0.229

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0430.166
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0230.036
Science and technology studies0.0130.006
Scholarly communication0.0360.014
Open science0.0040.010
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0190.007

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 head0.019
GPT teacher head0.246
Teacher spread0.228 · 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.

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

Citations4
Published2018
Admission routes1
Has abstractyes

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