Participatory Supervision in the 2024 Simultaneous General Election Stages in River area Communities in Barito Kuala Regency, Indonesia
Bibliographic record
Abstract
This research aims to measure the level of participation of river area communities in Barito Kuala Regency in monitoring the general election stages which include the stages of updating voter data and preparing the voter list in Barito Kuala Regency. The research method uses a quantitative approach with a descriptive survey method. This research was carried out in riverside villages in 2 sub-districts, namely Barambai Sub-district and Tabung Anen Sub-district with a total of 100 respondents in 18 villages. The research results describe that respondents who are not active play a role independently or in groups in monitoring the process of Voter data and the preparation of voter lists dominate more in Tabunganen District than those playing a very active role independently or in groups in monitoring the process. update voter data and preparation of voter lists. Meanwhile, community participation in Barambai District in preventing election monitoring officers from violating procedures or mechanisms in collecting community data for the 2024 elections is uneven. Meanwhile, a total of 87 respondents from the two sub-districts, both Tabunganen Subdistrict and Barambai Subdistrict, who never received information from other parties regarding alleged violations or election fraud at the stage of updating voter data and compiling the voter list in the 2024 election, were 11 respondent. Meanwhile for aspects reporting half of the survey respondents in Tabunganen District or 54% did not know/were not willing to provide information on alleged election violations, but a quarter of the respondents were hesitant/undecided and another quarter were willing to provide the information. Even if they did, half of the 50 respondents chose not to reveal the identity of the person providing information on alleged election violations, but a quarter, or 22% chose to only provide their initials and 26% chose to just notify.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".