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Authors, peer reviewers, and readers: What is expected from each player in collaborative publishing?

2021· article· en· W3119025180 on OpenAlexfundno aff
Fernando Fernández-Llimós

Bibliographic record

VenuePharmacy Practice · 2021
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsnot available
FundersUniversity of Illinois at Urbana-ChampaignUniversitas AirlanggaUniversità degli Studi di CamerinoUniversiti Sains MalaysiaUniversité LibanaiseUniversity of Technology SydneyUniversity of New EnglandBeirut Arab UniversityLomonosov Moscow State UniversityUniversidade de São PauloCairo UniversityRWTH Aachen UniversityUniversity of MinnesotaUniversity of PetraUniversity of TorontoUniversidade do PortoUniversidad Nacional de ColombiaDalhousie UniversityVrije Universiteit BrusselUniversité de ParisUniversidade Estadual de Ponta GrossaUniversidade de LisboaUniversità degli Studi Roma TreUniversitas PadjadjaranKøbenhavns Universitet
KeywordsPublishingnobodyQuality (philosophy)Open peer reviewVisibilityWork (physics)Peer reviewComputer sciencePublic relationsWorld Wide WebInternet privacyPolitical scienceLawEngineeringComputer securityEpistemology

Abstract

fetched live from OpenAlex

Scholarly publishing is in a crisis, with the many stakeholders complaining about different aspects of the system. Authors want fast publication times, high visibility and publications in high-impact journals. Readers want freely accessible, high-quality articles. Peer reviewers want recognition for the work they perform to ensure the quality of the published articles. However, authors, peer reviewers, and readers are three different roles played by the same group of individuals, the users of the scholarly publishing system-and this system could work based on a collaborative publishing principle where "nobody pays, and nobody gets paid".

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)0.004
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
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 score0.429
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.009
Open science0.0000.000
Research integrity0.0010.006
Insufficient payload (model declined to judge)0.0090.000

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 head0.485
GPT teacher head0.586
Teacher spread0.101 · 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 teacher head, 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

Citations13
Published2021
Admission routes1
Has abstractyes

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