PARTISIPASI PEMUDA DALAM KONTESTASI POLITIK DI KABUPATEN MAROS (STUDI KASUS : PEMILIHAN CALON ANGGOTA LEGISLATIF TAHUN 2019)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it