Russia was ‘Doomed to Expand [its] Aggression’ Against Ukraine: Cultural Property Criminals’ Responses to the Invasion and Occupation of the Donbas Since 20th February 2014
Bibliographic record
Abstract
This study explores how Russia’s invasion and occupation of Ukraine has affected cultural property crime and how cultural property criminals have responded to those practical, social, political and economic changes. To do so, this online ethnography draws on netnographic data from 184 artefact-hunters across Ukraine, Russia, Belarus, Greece, Germany, Belgium, the United Kingdom, the United States and Canada, two artefact-dealers and one violent political operator, whose discussions spanned 19 online communities. It examines the legal fictions and legal nihilism of antiquities looters; the criminal operations of antiquities looters and antiquities traffickers in the occupied territories of Ukraine; the international networks of artefact-hunters that facilitate the trading of equipment and antiquities, plus the movement of the artefact-hunters themselves and the conduct of their criminal operations. Thereby, it documents the pollution of Western markets with tainted cultural goods from the occupied territories of Ukraine and elsewhere in Eastern Europe and the contribution of Western consumers to the conflict economy.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".