Is reusing text from a protocol in the completed systematic review acceptable?
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.819 | 0.946 |
| Meta-epidemiology (narrow) | 0.004 | 0.012 |
| Meta-epidemiology (broad) | 0.017 | 0.016 |
| Bibliometrics | 0.016 | 0.022 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.032 | 0.046 |
| Open science | 0.011 | 0.018 |
| Research integrity | 0.039 | 0.029 |
| Insufficient payload (model declined to judge) | 0.033 | 0.027 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".