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Record W4362590789 · doi:10.1016/j.heliyon.2023.e15184

COVID-19 retracted publications on retraction watch: A systematic survey of their pre-prints and citations

2023· article· en· W4362590789 on OpenAlexaff
Zainab Syed, Fatema Syed, Lehana Thabane, Myanca Rodrigues

Bibliographic record

VenueHeliyon · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic integrity and plagiarism
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare HamiltonImpact
Fundersnot available
KeywordsMisinformationCoronavirus disease 2019 (COVID-19)Citation2019-20 coronavirus outbreakScientific misconductSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MedicineLibrary scienceAlternative medicineComputer sciencePathologyDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Background: Studies related to the coronavirus disease 2019 (COVID-19) were frequently published as pre-prints prior to undergoing peer-review. However, several publications were later retracted due to ethical concerns or study misconduct. Although these studies have been retracted, the availability of their corresponding pre-prints has never been formally investigated, and may result in the spread of misinformation if they are being used to inform decision-making. Methods: Our objective was to conduct a systematic survey of retracted COVID-19 publications listed on the Retraction Watch database as of August 15th, 2021. We assessed the availability of corresponding pre-prints for retracted publications, and documented the number of citations and online views. Results: Our study included 140 retracted COVID-19 publications, and we could not retrieve corresponding pre-prints for 132 retracted publications in our study (94%). Although we were unable to find the majority of pre-prints, they had already been disseminated, with a maximal citation count of 593 and Altmetric score of 558,928. Conclusion: While it is reassuring that most corresponding pre-prints could not be retrieved, our study highlights the need for online platforms and journals to employ quality assurance methods to prevent the spread of misinformation through citation of retracted papers.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearchResearch integrityBibliometrics
Domain: Evaluation · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
gptMetaresearchBibliometricsResearch integrity
Domain: Evaluation · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designhigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.005
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.790
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.116
GPT teacher head0.377
Teacher spread0.261 · 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

Labeled directly by 2 models reading the full record.

MetaresearchResearch integrityBibliometrics

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Other design
DomainEvaluation
GenreEmpirical

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

Citations12
Published2023
Admission routes1
Has abstractyes

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