Terrorism in post-Soviet space. Comparative analysis of the phenomenon in the former Sovi-et republics after 2014
Bibliographic record
Abstract
The post-Soviet states are also known as the former Soviet Republics (FSR). With the collapse of the USSR in 1991, Moscow lost almost a quarter of its territory and nearly 150,000,000 people. As a result of this process, 15 sovereign states emerged or reemerged. The post-Soviet states are very diverse in terms of culture, economy, and politics. Moreover, the phenomenon of terrorism varies in the indicated area. The research goal of this study is to identify trends related to terrorism taking place in the post-Soviet space in the years 2014-2020 (in some cases, the analysis covers the years 2015-2019, due to data availability). The research area covers the former Soviet republics, which are further divided by the author into four subregions (Eastern Europe, Central Asia, Transcaucasia, and Baltic states) that are linked by cultural and geopolitical factors. Therefore, the research object covers 15 states and 4 subregions.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".