Уровень 0 · материалов: 8
В кластер входят документы, использующие математические методы и анализ данных для выявления нарушений на российских выборах, и не входят документы, анализирующие статистику в других областях, таких как здравоохранение.
Общие признаки: выборы в России, статистические аномалии, фальсификация результатов голосования, математические доказательства манипуляций
Группа выше: Электронное голосование и выборы
Смысл: The main idea is that statistical anomalies in the 2018 Russian presidential election data suggest widespread falsification of results, which the author demonstrates through a custom-built data analysis tool.
The author uses data mining to reveal statistical anomalies indicating election fraud in the 2018 Russian presidential elections and shares the technical details of a tool created to visualize these patterns.
Смысл: The main idea is to use data science and visualization to highlight statistical irregularities in the 2016 Russian election results, suggesting that certain patterns are too unnatural to be coincidental and warrant official investigation.
A data-driven analysis of the 2016 Russian elections reveals suspicious statistical clusters and linear correlations in voting results across various regions.
Смысл: The main idea is that the presence of statistically impossible spikes at whole-number turnout percentages in the 2018 Russian elections proves systemic fabrication of results by election commissions.
Using a novel statistical iteration method, the author demonstrates that thousands of Russian election precincts fabricated turnout figures to hit round percentage targets.
Смысл: The main idea is that statistical analysis of the 2020 Russian constitutional vote reveals systemic falsification through the use of predetermined percentages, creating mathematical patterns that are virtually impossible in natural voting processes.
A data-driven investigation using statistical clustering and digit analysis to prove the systemic fabrication of the 2020 Russian constitutional amendment voting results.
Смысл: The main idea is to prove the existence of widespread electoral fraud in the 2020 Russian constitutional vote by demonstrating statistical anomalies—specifically unnaturally low variance in turnout and results—that cannot be explained by natural voting behavior.
The author uses data mining and Python to reveal statistical anomalies and improbable uniformity in the 2020 Russian constitutional vote, suggesting widespread artificial result generation.
Смысл: The main idea is to demonstrate how open data and statistical analysis can be used to identify anomalies and patterns in election results, specifically focusing on the 2011 Russian elections as a benchmark for the 2016 process.
The author provides a guide and statistical analysis of 2011 Russian election data using open sources to identify voting anomalies and prepare for the 2016 elections.
Смысл: The main idea is that the 2018 Primorsky Krai election was systematically stolen through a combination of physical protocol forgery and digital data manipulation in the official counting system. The author uses mathematical proofs to show that the government candidate's victory was artificial and that the actual winner was the opposition candidate.
A data-driven investigation using Python and Z3 proves that the 2018 Primorsky Krai gubernatorial election results were mathematically manipulated to replace the real winner with a government candidate.
Смысл: The text aims to expose technical irregularities and potential manipulations in the Russian Public Initiatives (ROI) voting system, arguing that the platform's internal mechanisms are opaque and potentially used to interfere with popular initiatives.
A technical investigation reveals systematic voting anomalies and counter manipulations on the ROI platform, suggesting internal interference rather than external bot attacks.