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Record W4398356913 · doi:10.7910/dvn/pqseqh

Data for: The impact of COVID-19 on the debate on open science

2024· dataset· en· W4398356913 on OpenAlexaff
Melanie Benson Marshall, Stephen Pinfield, Monique Batista de Oliveira, Natascha Chtena, Isabelle Dorsch, Juan Pablo Alperín, Mariana Hafiz, Anne Clinio, Germana Barata, Isabella Peters, Alice Fleerackers, Pamela Abbott, Andrew Cox

Bibliographic record

VenueHarvard Dataverse · 2024
Typedataset
Languageen
FieldDecision Sciences
TopicAcademic Publishing and Open Access
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakOpen scienceGeographyData scienceComputer scienceVirologyMathematicsBiologyStatisticsMedicine

Abstract

fetched live from OpenAlex

This dataset contains the appendix, analysis protocol, search strategy, and corpus of works included in the associated publication. A full description of the methodology can be found in the publication.

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)0.040
metaresearch head score (Gemma)0.118
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Open science, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.078
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0400.118
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0100.004
Open science0.1010.026
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.072

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 head0.261
GPT teacher head0.493
Teacher spread0.232 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
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
Published2024
Admission routes1
Has abstractyes

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