MétaCan
Menu
Back to cohort
Record W7135907026

Democratization in Europe between 1972 and 2000: Why it did not lead to War? Qualitative Comparative Analysis based on fuzzy set method

2013· dissertation· cs· W7135907026 on OpenAlexaboutno aff
Věra - Karin Brázová

Bibliographic record

VenueDigital Repository (National Repository of Grey Literature) · 2013
Typedissertation
Languagecs
FieldSocial Sciences
TopicQualitative Comparative Analysis Research
Canadian institutionsnot available
Fundersnot available
KeywordsDemocratizationQualitative comparative analysisDemocracyComparative politicsPoliticsSet (abstract data type)Quarter (Canadian coin)Qualitative analysis
DOInot available

Abstract

fetched live from OpenAlex

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,...

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.636
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0040.011
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.077
GPT teacher head0.440
Teacher spread0.363 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueDigital Repository (National Repository of Grey Literature)Same topicQualitative Comparative Analysis ResearchFrench-language works237,207