Democratization in Europe between 1972 and 2000: Why it did not lead to War? Qualitative Comparative Analysis based on fuzzy set method
Bibliographic record
Abstract
The rigorous thesis focuses on European countries which underwent so-called partial democratization in the last quarter of the 20th century. It starts from the polemic with Mansfield and Snyder who claim that a (partial) democratization leads to war. The development in Europe of the last quarter of the 20th century, however, seems to contradict this notion. The aim of the thesis is, thus, to contribute to the debate of war-proneness of democratizing states by answering the following question: What caused that the democratization did not lead to war in many cases? Due to the nature of the research question as well as to the number of cases (i.e. 20) the method applied here is qualitative comparative analysis using the so-called fuzzy set method. The application of this method as such is a secondary aim of the thesis. Possible causal conditions of the absence of war which are under study here also derive mostly from the conclusions made by Mansfield and Snyder. The main focus is put on the so-called golden parachute. Among other causes are strong institutions - conceptualized here as weak and weakened executive, political integration into international community, duration of independent statehood and at least some experience with democracy - and developed economy - conceptualized through GDP,...
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".