Misuse of Uniforms, Emblems, Flags, Insignia, and the Ukraine Conflict
Bibliographic record
Abstract
Misuse of Uniforms, Emblems, Flags, Insignia, and the Ukraine Conflict examines how the 2022 invasion of Ukraine has renewed the relevance of international humanitarian law governing the misuse of uniforms, emblems, flags, and insignia in modern inter-state warfare. It documents allegations that parties have worn enemy or neutral uniforms, used protected insignia such as Red Cross markings, or engaged in other deceptive measures that may constitute treachery or perfidy. The author analyzes the legal definitions and prohibitions under the law of armed conflict regarding improper use of distinctive signs for deception, distinguishing between lawful ruses and forbidden perfidious conduct. The work also addresses the challenges of accountability when both sides may engage in similar deceptive practices, raising potential tu quoque issues. Ultimately, Misuse of Uniforms, Emblems, Flags, Insignia, and the Ukraine Conflict highlights how such reciprocal deception complicates the successful prosecution and enforcement of international norms in the Ukraine conflict.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".