The COPE / DOAJ / OASPA / WAME Principles of Transparency and Best Practice in Scholarly Publishing: A Critical Analysis
Bibliographic record
Abstract
Four publishing-related organizations, the Committee on Publication Ethics (COPE), the Directory of Open Access Journals (DOAJ), the Open Access Scholarly Publishers Association (OASPA), and the World Association of Medical Editors (WAME), the first being dedicated specifically to the creation and dissemination of ethics policies, established a set of 16 principles related to journal and publisher transparency and “best” publishing practices. The first, second, third and fourth versions were published in 2013, 2015, 2018, and 2022, respectively. Membership of these organizations implies that members can only become such if they satisfy these principles. This paper compares the four versions to appreciate how the content has changed over time, as a historical endeavor to gather how publishing ethics has progressed over time. An assessment is also made to determine whether all principles are related to transparency and best principles, and if any may be missing. We concluded that the 16 principles offer broad guidance to several important aspects related to journal and publishing ethics and management. However, the vast majority are in general excessively broad, occasionally vague, or lack sufficient examples or specifics, despite the slight improvement between versions 3 and 4. We argue further that these weaknesses may limit their practical application. Until September 2022, there was no transparency regarding the consequences for any members that might violate, or not abide by, these principles. In the light of these arguments, we are of the opinion that the 16 principles of “best” publishing practices merit additional improvements.
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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 | 0.265 | 0.344 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.022 | 0.013 |
| Science and technology studies | 0.011 | 0.053 |
| Scholarly communication | 0.029 | 0.023 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".