Application Of Vikor Method To Determine The Location Of Election And Election Care Villages
Bibliographic record
Abstract
General elections are the core of the democratic process which involves active community participation. In order to realize optimal participation, it is necessary to make efforts to increase public awareness and participation in elections and elections. One effective way is to identify strategic locations to implement the "Village Care for Elections and Elections" program. This study aims to apply the VIKOR method (VlseKriterijumska Optimizacija I Kompromisno Resenje) in determining the optimal location for the program. The VIKOR method is used as an analytical tool in considering several relevant criteria, such as the level of previous participation, the level of political awareness, the accessibility of the location, and the level of local government support. These data are evaluated and analyzed to assign a ranking to each potential location. Application steps include data collection, normalization, calculating VIKOR scores, and determining ranking. The results of this research provide clear guidance in determining the most suitable location for the "Election and Election Care Village" program. The selected location is the result of a compromise that considers all relevant criteria. It is hoped that the results of this research can become a basis for local governments or related institutions to allocate resources more effectively in order to increase people's participation in the democratic process.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".