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Record W3157349901 · doi:10.1186/s13643-021-01675-9

Is reusing text from a protocol in the completed systematic review acceptable?

2021· letter· en· W3157349901 on OpenAlexaff
Dawid Pieper, Long Ge, Ahmed M Abou-Setta

Bibliographic record

VenueSystematic Reviews · 2021
Typeletter
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsUniversity of ManitobaGeorge & Fay Yee Centre for Healthcare Innovation
Fundersnot available
KeywordsCopyingProtocol (science)Context (archaeology)ReuseMedicinePerspective (graphical)Work (physics)Engineering ethicsComputer scienceAlternative medicineArtificial intelligencePathologyLaw

Abstract

fetched live from OpenAlex

Published protocols have the potential to reduce bias in the conduct and reporting of systematic reviews (SR). When reporting the results of a completed SR, the question might arise whether text used in the protocol can also be used in the completed SR? Does this constitute text recycling, plagiarism, or even copyright infringement? In theory, no major changes to the protocol will be expected for the introduction and methods sections if the SR is completed in time. The benefits of maintaining the introduction and methods section of a protocol in the published SR are straightforward. Authors will require less time for writing up the completed SR. Potential benefits can also be expected for peer reviewers and editors. However, reusing text can be described as self-plagiarism. The question to be answered is whether this type of self-plagiarism is acceptable when copying text used previously (as would be the case when copying text from the protocol and pasting it into the subsequent completed SR)? The "traditional answer" to this question is "yes" because authors should not get credit for one piece of work for more than one time unless the work is cited appropriately. In contrast, we propose that in this context, it seems to be fully acceptable from a scientific and ethical perspective. As such, authors should not be accused of plagiarism in this case, but rather be encouraged to be efficient. However, legal issues need to be taken into consideration (e.g., copyright). We hope to stimulate a discussion on this topic among authors, readers, editors, and publishers.

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)0.420
metaresearch head score (Gemma)0.274
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.339
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.4200.274
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0670.015
Bibliometrics0.0010.008
Science and technology studies0.0000.000
Scholarly communication0.0060.001
Open science0.0170.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0340.037

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 head0.782
GPT teacher head0.545
Teacher spread0.237 · 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 designSystematic review
Domainnot available
GenreProtocol

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

Citations7
Published2021
Admission routes1
Has abstractyes

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