Factors and Determinants of Political Participation of Ethnic Groups
Bibliographic record
Abstract
This paper discusses the problem of identifying factors and determinants of the political participation of ethnic groups in politics. Analysis of scientific literature allows us to identify several approaches to solving this problem. Some people view the political participation as an activity by which individuals try to influence the government through ethnic groups so that it takes the actions they want. This impact on the processes of political decision-making and the implementation of political programs related to them. Others believe that the driver of political activity is the need for internal improvement of an individual, when political participation contributes to their full functioning in the life of the state and gives them a sense of involvement in political processes. A comprehensive approach to determining the essence of the political participation of ethnic groups will be justified, according to which the institution of political participation is a multifaceted sociocultural phenomenon that affects many aspects of the socio-political dynamics of modern society. In accordance with this approach, political participation is equally manifested in both democratic and non-democratic political regimes; at the same time, the trigger of political mobilization can be not only the impact of political leaders, but also their own need for people to actively participate in political processes.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".