Clearer Academic Guidelines to Assist Authors and Editors Are Needed to Navigate Geopolitically Sensitive Conflicts
Bibliographic record
Abstract
There are ample national, transnational, and international geopolitical conflicts around the world, the two most prominent likely being the current ongoing Russo-Ukrainian war, and the conflict between Taiwan (the Republic of China) and China (the People’s Republic of China). To avoid criticism or the label of political bias, academics might refrain from using the term Taiwan or might not refer to it as a country. Similarly, they might not know whether they should refer to Crimea or the Donbas as being Ukrainian or Russian. Authors currently have little guidance and are somewhat left to their own devices when it comes to referring to these and other locations and territories, uncertain of the names that should be used to indicate them. They might also observe disclaimers on publishers’ websites, in the footer of editorial board pages, or even as a small notice in their own manuscripts placed there by the publisher that distance the publisher from geopolitical conflicts and/or territorial claims, claiming neutrality, independent of whether those papers mention those conflicts, or not. Such disclaimers might be perceived as self-serving, placing the onus of responsibility of the choice of territorial term on authors’ shoulders, so publishers should offer clearer advice to authors and editors on how to better handle this issue, that is, how to accurately name locations in geopolitically sensitive areas. There is a risk that papers that are insufficiently sensitive to such issues may be labelled as erroneous, and subjected to correction or retraction, or the authors may be subjected to public criticism or humiliation, so resolving this issue falls within the realm of academic publishing ethics. Currently, very little advice exists for geopolitical issues in the Committee on Publication Ethics and International Committee of Medical Journal Ethics ethics-related publishing guidelines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 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; a candidate call from one teacher head, not a consensus.
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".