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Record W2227311185 · doi:10.1177/1032373215602081

Anatomy of a journal: A reflection on the evolution of <i>Contemporary Accounting Research</i> , 1984–2010

2015· article· en· W2227311185 on OpenAlexafffundabout
Irene M. Gordon, Lawrence A. Boland

Bibliographic record

VenueAccounting History · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting Education and Careers
Canadian institutionsSimon Fraser University
FundersCanadian Academic Accounting Association
KeywordsPublicationHomogeneousAccounting researchAccountingQuality (philosophy)SociologyHistoryPolitical scienceEpistemologyEconomicsLaw

Abstract

fetched live from OpenAlex

Our study outlines the evolution of a highly rated accounting journal, Contemporary Accounting Research ( CAR). We examine two tensions (high quality, global journal versus Canadian authorship and homogeneous versus diverse research) that arose during CAR’s history, using Canadian Academic Accounting Association documents (CAAA) and evidence from the main articles published in CAR’s first 27 volumes. We address three research questions relevant to exploring the identified tensions: Where have CAR’s published authors been concentrated in terms of geographical location? What types of research have been published in CAR over the period? How well has CAR succeeded in meeting its original and later editorial objectives? With respect to published main articles, our findings indicate that being a high quality, global journal has won over promoting Canadian authors and that articles published in CAR tend to be more homogeneous than might be expected from the original objectives and later editorial statements. Our findings should be relevant to those interested in the history of accounting research and to those trying to publish in CAR.

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)0.022
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score0.606

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.048
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0100.014
Science and technology studies0.0190.042
Scholarly communication0.0340.010
Open science0.0010.006
Research integrity0.0030.005
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 head0.099
GPT teacher head0.315
Teacher spread0.217 · 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 designNot applicable
DomainEvaluation
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

Citations16
Published2015
Admission routes3
Has abstractyes

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