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Record W2766235906 · doi:10.5167/uzh-149872

Data sharing: a new editorial initiative of the international committee of medical journal editors. implications for the editors' network

2017· article· en· W2766235906 on OpenAlexaff
Fernándo Alfonso, Karlen Adamyan, Jean‐Yves Artigou, Michael Aschermann, Michael Boehm, Alfonso Buendía-Hernández, Pao‐Hsien Chu, Ariel Cohen, Livio Dei, Mirza Dilić, Anton Doubell, Darío Echeverri, Nuray Enç, Ignacio Ferreira‐González, Krzysztof J. Filipiak‬, Andreas J. Flammer, Eckart Fleck, Plamen Gatzov, Carmen Ginghină, Lino Gonçalves, Habib Haouala, Mahmoud Hassanein, Gerd Heusch, Kurt Huber, I Hulín, Mario Ivanuša, Rungroj Krittayaphong, Chu-Pak Lau, Germanas Marinskis, François Mach, Thomas F. Lüscher, Tuomo Nieminen

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsLibrary and Archives Canada
Fundersnot available
KeywordsMedical journalMedicineData sharingEngineering ethicsClinical trialAccountabilityMedical educationLibrary scienceAlternative medicinePublic relationsPolitical scienceFamily medicineLawPathologyComputer scienceEngineering

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.352
metaresearch head score (Gemma)0.571
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Open science
Consensus categoriesMetaresearch
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.988
Threshold uncertainty score0.799

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3520.571
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0060.005
Bibliometrics0.0070.006
Science and technology studies0.0200.014
Scholarly communication0.0550.029
Open science0.0120.013
Research integrity0.0460.061
Insufficient payload (model declined to judge)0.0180.009

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 head0.607
GPT teacher head0.556
Teacher spread0.051 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainReproducibility
GenreEditorial

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

Citations1
Published2017
Admission routes1
Has abstractno

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