Women Politicians and Parliamentary Elections in Ukraine and Georgia in 2012
Bibliographic record
Abstract
Abstract: Post-communist countries undergoing social transformations in the last twenty years needed to implement political and economic reforms. Changes also had to support the principles of equality in the access to power, specifically gender quotas in executive and legislative branches of government and within political parties. The events in Ukraine and Georgia in 2004-2005 known as the “colour revolutions” gave impulse to the promotion of equality and implementation of reforms. However, the number of women participating in national politics in both countries remains low. This paper proposes an analysis of gender equality principles during the parliamentary election campaigns in Ukraine and Georgia in 2012 from the perspective of women’s participation in politics and their self-representation as politicians. This empirical study covers public attitudes towards women in politics and examines networks of female parliamentarians. The findings raise hopes for better representation of women in politics as female politicians promote them from the top down, and mass public perception of gender equality principles set the ground for bottom-up activism. Keywords: Gender Equality, Women Politicians, Public Attitudes, Social Network Analysis (SNA)
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".