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Measuring Canadian Business School Research Output and Impact

2002· article· fr· W2141830052 on OpenAlexaffvenueabout
Erhan Erkut

Bibliographic record

VenueCanadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration · 2002
Typearticle
Languagefr
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHumanitiesPer capitaPolitical scienceCitationProduction (economics)Knowledge productionCitation impactEconomicsSociologyPhilosophyComputer scienceMicroeconomicsDemographyKnowledge management

Abstract

fetched live from OpenAlex

Abstract The goal of this paper is to quantify both the output and the impact of the past decade's scholarly research carried out by those academics currently employed by Canadian business schools, using journal paper counts and citation analysis. We find that the per capita paper output in Canadian business schools is relatively low and is declining. We also find that there are significant differences across Canadian business schools, and that the paper and citation credits are highly variable, with a few “stars” producing most of the impact. Résumé Dans cet article nous essayons de mesurer le rendement et l'impact de la recherche académique dans les écoles canadiennes d'affaires pendant la dernière décennie, en utilisant le comptage des articles publiés et le nombre de citations dans un ensemble des revues spécialisées. Nous constatons que la production des articles par les écoles canadiennes est relativement basse et que la tendance va en empirant. De plus, nous constatons qu'il y a des différences significatives parmi les écoles canadiennes en termes de production académique, avec quelques chercheurs responsables pour la majorité de la production totale.

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)0.065
metaresearch head score (Gemma)0.076
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Bibliometrics, Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Bibliometrics, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0650.076
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0510.119
Science and technology studies0.0080.025
Scholarly communication0.0140.005
Open science0.0050.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.777
GPT teacher head0.535
Teacher spread0.242 · 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; both teacher heads agree on what is shown here.

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

Citations54
Published2002
Admission routes3
Has abstractyes

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