Voter Participation in the Election of the Head and Deputy Regional Head of Banjarmasin City in 2020 During the Covid 19 Pandemic
Bibliographic record
Abstract
The purpose of this study is to find out and explain the causes of low voter participation in the election of the head and deputy regional head of Banjarmasin City in 2020 during the covid 19 pandemic. This study uses a qualitative approach with descriptive research type, data collection techniques using interviews and documentation, data analysis techniques data reduction, data presentation, conclusion drawing or verification, and the validity of the data source triangulation, technique mastulation and time triangulation. The results showed that the low voter participation in the election of the Head and Deputy Regional Head of Banjarmasin City in 2020 during the Covid 19 Pandemic was influenced by factors, namely the covid 19 factor, the weather/rain factor, the administrative factor, the work factor, and the habit factor. The low participation was due to not achieving the target set by the general election commission of the Republic of Indonesia, namely 77.5%, the target of the general election commission of Banjarmasin city of 75% was only achieved by 57.63% This research is expected to be a suggestion to election organizers to increase public awareness of the importance of voter participation in the 2020 Banjarmasin City Head and Deputy Regional Head Elections in the Covid 19 Pandemic Period in order to achieve the set targets.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".