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Record W2909441134 · doi:10.1177/1748048518825322

Editorial boards in communication sciences journals: Plurality or standardization?

2019· paratext· en· W2909441134 on OpenAlexaboutno aff
Manuel Goyanes

Bibliographic record

VenueInternational Communication Gazette · 2019
Typeparatext
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAcknowledgementTypologyDiversity (politics)Status quoValue (mathematics)SociologySocial scienceCitationPolitical sciencePublic relationsLawAnthropology

Abstract

fetched live from OpenAlex

This research-based essay examines the national diversity of editorial boards from a selection of journals in communication sciences. Specifically, it reviews the board composition of 39 Journal Citation Report journals indexed in quartile one (Q1) and quartile two (Q2) in the category of ‘communication’, proposing a typology of dominant nationalities. The most distinguished countries are the United States, United Kingdom, Canada, Australia and Germany, monopolizing 79.4% of total members. The exaggerated domination of certain geographies is surprising given the increasing acknowledgement of plurality as a constitutive value of scientific progress. The article then problematizes why plurality is limited and, therefore, identifies a body of social and cultural bonds that underpin the domination of certain epistemic cultures. The study finally proposes an agenda that moves beyond the current status quo, and considers how these actions are likely to promote a more pluralistic and diverse intellectual terrain.

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)0.035
metaresearch head score (Gemma)0.125
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.965
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.125
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0080.010
Science and technology studies0.0090.015
Scholarly communication0.0280.011
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.041
GPT teacher head0.334
Teacher spread0.293 · 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
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

Citations48
Published2019
Admission routes1
Has abstractyes

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