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Record W4415377881 · doi:10.36591/se-4204-01

International Perspectives

2019· article· en· W4415377881 on OpenAlexaboutno aff
Jonathan Schultz

Bibliographic record

VenueScience Editor · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicAcademic Publishing and Open Access
Canadian institutionsnot available
Fundersnot available
KeywordsEditorial boardPublishingPublicationDiversity (politics)Representation (politics)Bibliometrics

Abstract

fetched live from OpenAlex

What does it mean to be an international journal? In an online publishing and submission landscape, almost all journals, even those with a specific location in their title, are effectively international in the sense that they may have readers around the world and accept submissions for any researcher, anywhere. But is that enough to be truly international? In this issue of Science Editor, three articles explore what it means to be an international, geographically diverse journal or organization and provide suggestions for improvement. Global Balance As discussed in a recent Science Editor Newsletter,1 The makeup of an editorial board is an area where journals may try to improve their international reach, adding members from across the globe. But as Rafael Araújo and Geoffrey Shideler report in the article based on their award-winning abstract from the CSE 2019 Annual Meeting, Cultural and Geographical Representation in the Editorial Boards of Aquatic Science Journals, the extent of many editorial board’s geographic diversity does not always compare to the geographic diversity of their authors. Specifically looking at aquatic science journals, Araújo and Shideler find that some countries, particularly the US, have an overrepresentation on editorial boards whereas others, particularly China, are underrepresented compared to how often authors from those countries publish in the journal. The authors refer to this difference as either an editorial surplus or deficit and provide a framework for determining where a journal stands: take the geographic representation of the editors (e.g., 50% US-based editors, 20% Canada, 10% Japan) and compare […]

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)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.688

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0050.004
Scholarly communication0.0150.010
Open science0.0020.007
Research integrity0.0080.009
Insufficient payload (model declined to judge)0.2060.058

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 head0.037
GPT teacher head0.414
Teacher spread0.377 · 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
Domainnot available
GenreCommentary

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

Citations0
Published2019
Admission routes1
Has abstractyes

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