Exploring reviewer self-assessment in the context of academic peer review.

Amanda Sizo, Alexandra Oliveira, Adriano Lino, Álvaro Rocha, Luís Paulo Reis

Abstract read

In one paragraph

Article in Scientific reports, 2025. 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

5 authors.

Amanda SizoLIACC - Artificial Intelligence and Computer Science Laboratory, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias S/N, 4200-465, Porto, Portugal. up202010567@edu.fe.up.pt.

Alexandra OliveiraPolytechnic Institute of Porto School of Health, Polytechnic Institute of Porto, Porto, Portugal.

Adriano LinoInstitute of Engineering and Geosciences, Federal University of Western Pará, Santarém, Brazil.

Álvaro RochaFaculty of Economics and Business, Brawijaya University, Malang, Indonesia.

Luís Paulo ReisLIACC - Artificial Intelligence and Computer Science Laboratory, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias S/N, 4200-465, Porto, Portugal.

Funding

Artificial Intelligence and Computer Science Laboratory - LIACC - funded by national funds through the FCT/MCTES (PIDDAC) UID/00027Fundação para a Ciência e a Tecnologia PD/BD/2022.13474.BD

6 · The paper itself

Abstract

Scientific journals often rely on informal methods to evaluate reviewers, such as editor ratings and author feedback. Reviewer self-assessment offers a promising, yet underexplored, approach to improving the peer-review process. This study examined the factors associated with reviewers' self-assessments. We surveyed 642 reviewers and editors from three Information Systems (IS) conferences (January-February 2020), and 144 responses were analyzed using quantitative inferential statistics. Most respondents were male (72.2%) and based in Europe (59%). We found no significant association between self-assessment and conventional experience markers (reviewing and publishing experiences). In contrast, significant associations were observed between higher self-assessment and the perceived importance of feedback from editors (χ

Indexed as

Peer ReviewPeer Review, ResearchSelf-AssessmentAdultFemaleHumansMaleMiddle AgedPublishingSurveys and QuestionnairesEmpirical studyPeer-reviewReviewer experienceSelf-assessmentSurvey questionnaire

Identifiers

PMID41188326

DOI10.1038/s41598-025-22352-0

PMCPMC12586671

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

Read underepoch 390

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.