PARTISIPASI PEMUDA DALAM KONTESTASI POLITIK DI KABUPATEN MAROS (STUDI KASUS : PEMILIHAN CALON ANGGOTA LEGISLATIF TAHUN 2019)
Bibliographic record
Abstract
This study examines the factors that influence youth participation in legislative elections inMaros regency in 2019. The participation of youth is as participants in the election of a political party.Based on data from the Regional Election Commission (Election Commission) Maros has recorded asmany as 105 young men involved as a legislative candidate Maros regency in 2019. The method usedin this research is qualitative method with descriptive models. The data collection was done byinterview, observation, and focus group discussion (FGD). The informants came from variousbackgrounds political parties and youth organizations. Determination of informants were chosenintentionally (purposive). This study was conducted in Maros, from December 2019 until February2020. The results showed that the factors that influence youth participation in legislative electionscontestation is divided into three: the idealism of youth, power relations and capital, as well as thesupplementary administrative requirements. 1) pertaining to the idealism of youth and youthorganizations during a student activities on campus. 2) The power relations and capital, capitalutilization and the persona of the parents of the young man to come forward to fight. 3) Complementaryadministrative requirements, relating to the regulation of elections that take advantage of youth toparticipate as election participants and carried out by a political party elite as well as the supplementaryadministrative requirements.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".