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A review of the literature on ethical issues related to scientific authorship
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The article at hand presents the results of a literature review on the ethical issues related to scientific authorship. These issues are understood as questions and/or concerns about obligations, values or virtues in relation to reporting, authorship and publication of research results. For this purpose, the Web of Science core collection was searched for English resources published between 1945 and 2018, and a total of 324 items were analyzed. Based on the review of the documents, ten ethical themes have been identified, some of which entail several ethical issues. Ranked on the basis of their frequency of occurrence these themes are: 1) attribution, 2) violations of the norms of authorship, 3) bias, 4) responsibility and accountability, 5) authorship order, 6) citations and referencing, 7) definition of authorship, 8) publication strategy, 9) originality, and 10) sanctions. In mapping these themes, the current article explores major ethical issue and provides a critical discussion about the application of codes of conduct, various understandings of culture, and contributing factors to unethical behavior.
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“Bias mostly occurs unintentionally (i.e., subconsciously); for example, when a scientist underpowers a study or is not aware of sexist assumptions that underpin their interpretive framework [48–52].”
From Autonomous artificial intelligence, scientific research, and human values (2026) · cites this paper as [48]
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“For example, current AI tools can detect plagiarism of words but not other types of plagiarism, such as misappropriation of data, ideas, methods, arguments, images, tables, or unique forms of expression (Hosseini and Gordijn, 2020).”
Self-citationGoddiksen, M. P., Johansen, M. W., Armond, A. C. V., Clavien, C., Hogan, L., Kovács, N., Merit, M. T., Olsson, I. A. S., Quinn, U., Santos, J., et al. (2023) · PLoS ONE · doi:10.1371/journal.pone.0280018 · Cited by 44
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Metadata
- Authors
- Hosseini, M., & Gordijn, B.
- Affiliations
- Dublin City University (IE)
- Corresponding author
- Hosseini, Mohammad
- Venue
- Accountability in Research
- Publisher
- Informa UK Limited
- Published
- 2020-04-16
- Volume
- 27
- Issue
- 5
- Pages
- 284-324
- ISSN
- 0898-9621 (ISSN-L)
- 1545-5815
- OpenAlex
- W3014226787
- Cited by
- 38
- References
- 246
- Citations by year
- 2026: 6 · 2025: 5 · 2024: 10 · 2023: 3 · 2022: 10 · 2021: 4
- FWCI
- 0.82
- Type
- journal article
- Language
- en
- Open access
- green
- doras.dcu.ie
- Licence
- cc-by-nc-sa
- Locations
- Accountability in Research (publishedVersion)
- PubMed (publishedVersion)
- Dublin City University Open Access Institutional Repository (Dublin City University) (submittedVersion, cc-by-nc-sa)
- Arrow@dit (Dublin Institute of Technology) (submittedVersion)
- Funders
- European Commission (741782)
- University of Hong Kong
- Horizon 2020 Framework Programme (741782)
- Topics
- Social Sciences › Education › Academic integrity and plagiarism
- Social Sciences › Library and Information Sciences › Publishing and Scholarly Communication
- Social Sciences › Library and Information Sciences › scientometrics and bibliometrics research
- Keywords
- scientific authorship, research ethics, authorship norms, attribution, authorship order, publication ethics, citation practices, originality, codes of conduct, research bias
- MeSH terms
- Authorship, Humans, Periodicals as Topic, Punishment, Plagiarism, Scientific Misconduct, Guidelines as Topic, Ethics, Research
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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