Evidence map›Paper›PMID 36741346›Full record

ArticleFrontiers in research metrics and analytics2023

Are female scientists underrepresented in self-retractions for honest error?

Mariana D Ribeiro, Jesus Mena-Chalco, Karina de Albuquerque Rocha, Marlise Pedrotti, Patrick Menezes, Sonia M R Vasconcelos

Abstract read
In one paragraph

Article in Frontiers in research metrics and analytics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Mariana D RibeiroScience Education Program, Institute of Medical Biochemistry Leopoldo de Meis (IBqM), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil.
Jesus Mena-ChalcoCenter for Mathematics, Computing and Cognition (CMCC), Federal University of ABC (UFABC), São Paulo, Brazil.
Karina de Albuquerque RochaScience Education Program, Institute of Medical Biochemistry Leopoldo de Meis (IBqM), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil.
Marlise PedrottiScience Education Program, Institute of Medical Biochemistry Leopoldo de Meis (IBqM), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil.
Patrick MenezesScience Education Program, Institute of Medical Biochemistry Leopoldo de Meis (IBqM), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil.
Sonia M R VasconcelosScience Education Program, Institute of Medical Biochemistry Leopoldo de Meis (IBqM), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Retractions are among the effective measures to strengthen the self-correction of science and the quality of the literature. When it comes to self-retractions for honest errors, exposing one's own failures is not a trivial matter for researchers. However, self-correcting data, results and/or conclusions has increasingly been perceived as a good research practice, although rewarding such practice challenges traditional models of research assessment. In this context, it is timely to investigate who have self-retracted for honest error in terms of country, field, and gender. We show results on these three factors, focusing on gender, as data are scarce on the representation of female scientists in efforts to set the research record straight. We collected 3,822 retraction records, including research articles, review papers, meta-analyses, and letters under the category "error" from the Retraction Watch Database for the 2010-2021 period. We screened the dataset collected for research articles (2,906) and then excluded retractions by publishers, editors, or third parties, and those mentioning any investigation issues. We analyzed the content of each retraction manually to include only those indicating that they were requested by authors and attributed solely to unintended mistakes. We categorized the records according to country, field, and gender, after selecting research articles with a sole corresponding author. Gender was predicted using

Indexed as

genderresearch assessmentresearch integrityretractionsscience policyself-correction of science

Identifiers

PMID36741346
PMCPMC9895951

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.