The Regional Life of Geopolitical Conflict: The Case of Odes(s)a Oblast
Bibliographic record
Abstract
When the events known as the “Russian Spring” began in the aftermath of Ukraine’s Euromaidan Revolution in February 2014, Odessa oblast seemed like it would be particularly vulnerable to separatist activity. This paper offers a tentative explanation for why Odessa oblast escaped war in the 2014–15 phase of the Russia-Ukraine conflict. To do so, it chronicles events in Odessa oblast between fall 2013 and spring 2015 drawing on secondary sources such as news articles, blogs, social media posts, YouTube footage, official statements, and reports. Odessa-based elites’ decision to support Ukrainian sovereignty was an important factor hindering the realization of a Donetsk or Luhansk scenario. However, the weak Oblast Administration in the spring of 2014 and the upcoming mayoral elections created a volatile environment that various individuals (oligarchs, politicians, criminal networks) exploited to maintain or enhance their influence in the region. The internationalization of the conflict in the spring of 2014 presented Odessans with stark existential choices which undermined the city’s violence-avoiding dispute resolution techniques and culminated in the violence on May 2.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".