Distribution of Deputy Mandates: Analysis of Proportional Representation in the Context of a Mixed Electoral System
Bibliographic record
Abstract
Elections are a socio-political institution, during which holding it is determined what the nature of the reforms will be in the next few years. It is important that the legitimacy of the electoral process is high and that key reforms are determined by competent government officials. The basic element of elections is a high level of competition, which should exist not only between various political entities that exercise eligibility to vote and right to be elected but also within such structures fighting for power. The paper contains an analysis concerning the issue on the functioning of the proportional vote distribution institute. According to the election results, it is necessary to determine how many seats will go to a certain party, which, according to the proportional system, has overcome the percentage barrier. In world practice, there is a whole range of proportional distribution methods that form two large groups: the largest remainder methods and the dividers methods. There are discussions on this parameter, and each country has adopted its own methodology. In Russia, with a proportional distribution of seats, one of the largest remainder methods is used, namely, the Hare method. The study will reflect the analysis of the functioning of proportional distribution systems in Russia and in the world.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".