Criminalistics Means and Methods of Combating Ecocide in the Modern Conditions of Military Threats
Bibliographic record
Abstract
The purpose of this study was to develop an integrated approach to the implementation of criminalistics tools and methodology to prevent and counteract large-scale environmental destruction in the context of modern military threats. To fulfil this purpose, the study analysed the legal aspects of regulating ecocide as a war crime, assessed the effectiveness of existing forensic tools and methods, and investigated the judicial practice and statistics of environmental crimes in Ukraine for 2014-2024. The study found that the number of reported cases of environmental crimes in the conflict zone increased by 73% during this period, with a strong correlation (r=0.82) between the intensity of hostilities and the number of cases of ecocide. The expert survey showed that the most effective forensic tools for detecting and documenting environmental crimes are satellite monitoring, geographic information systems, and unmanned aerial vehicles. The analysis of 75 court decisions showed that in 68% of cases, the actions were classified as ecocide, but in 22% – as other environmental crimes, which indicates the difficulty of proving all elements of the crime of ecocide. Based on the findings obtained, comprehensive recommendations were developed to improve legal regulation, institutional support, technological equipment, investigation methods, and international cooperation in the field of combating ecocide in armed conflicts. Specifically, it was proposed to amend national legislation to clearly define the crime of ecocide, strengthen the institutional capacity of the authorised bodies, expand the use of modern technologies for monitoring and recording environmental crimes, and intensify international cooperation in this area.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".