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Record W4393446218 · doi:10.5281/zenodo.3826038

Open Access of COVID-19 related publications in the first quarter of 2020: a preliminary study based in PubMed

2020· dataset· en· W4393446218 on OpenAlexaboutno aff
Olatz Arrizabalaga, David Otaegui, Itziar Vergara, Julio Arrizabalaga, Eva Méndez

Bibliographic record

VenueFigshare · 2020
Typedataset
Languageen
FieldDecision Sciences
TopicAcademic Publishing and Open Access
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Coronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakVirologyMedicineHistoryInfectious disease (medical specialty)Internal medicineArchaeology

Abstract

fetched live from OpenAlex

Underlying data of the article: "Open Access of COVID-19 related publications in the first quarter of 2020: a preliminary study based in PubMed". Data was analysed using Unpaywall and OpenRefine.

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)0.009
metaresearch head score (Gemma)0.074
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Open science
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.998
Threshold uncertainty score0.242

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.074
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0170.025
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0720.023

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 head0.304
GPT teacher head0.478
Teacher spread0.174 · 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
DomainReproducibility
GenreDataset

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
Published2020
Admission routes1
Has abstractyes

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