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Record W1628131229 · doi:10.1111/caje.12077

Incentives for Journal Editors

2014· article· en· W1628131229 on OpenAlexvenueno aff
Jinyoung Kim, Kanghyock Koh

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2014
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsnot available
Fundersnot available
KeywordsIncentiveSelection (genetic algorithm)CitationComputer scienceTest (biology)Positive economicsLibrary scienceData scienceEconomicsArtificial intelligenceBiology

Abstract

fetched live from OpenAlex

Abstract Scholars may become journal editors because editors may generate more citations of their own works. This paper empirically establishes that a scholar's publications are more likely to be cited by papers in a journal that is edited by the scholar. We then test if editors exercise influence on authors to cite editors’ papers by either pressuring authors (“editor‐pressure” hypothesis) or accepting articles with references to the editors’ papers (“editor‐selection” hypothesis), by using the keyword analysis and the forward citation analysis, respectively. We find no evidence for the two hypotheses, which leaves self‐selection as a possible cause for the editor effect. JEL classification: J01

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)0.015
metaresearch head score (Gemma)0.148
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.985
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.148
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0070.003
Open science0.0010.002
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0290.005

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 head0.617
GPT teacher head0.376
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainIncentives
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

Citations9
Published2014
Admission routes1
Has abstractyes

Explore more

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