State building in the middle of a geopolitical struggle: The cases of Ukraine, Moldova and Transnistria.
Bibliographic record
Abstract
This research intends to know if it is possible for Ukraine, Moldova and Pridnestrovia to build a sovereign state in the current global context, while in the middle of a geopolitical struggle. For the theoretical part, it considers Schmitt notions of sovereignty and portrays some of the main geopolitical schools of thought in the US and Russia to understand each of the dominant geopolitical projects in the area. It analyses the state of the relation between Russia and the West (including the US, the EU and its members) from a geopolitical perspective and the way it has influenced the political events in the area. Afterwards, it examines the historical and political developments mostly from the fall of the Soviet Union until the first quarter of 2016. This investigation also took into account the complex multi-ethnic and multilingual tissue in the area and the development of diverse electoral processes in the region. It underlined the role played by the Ukrainian oligarchs in the local political life and the effects it has brought in the same state-building process. It gives special attention to the events surrounding the Ukrainian crisis in 2013-14. To conclude, it intends to glimpse, in which direction are evolving each of the studied polities and if they could eventually develop into sovereign states or instead they risk of becoming failed states.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".