Kinerja Komisi Pemilihan Umum Daerah dalam Menyelenggarakan Pemilihan Kepala Daerah di Kabupaten Sumba Barat Daya Tahun 2013
Bibliographic record
Abstract
Election is the most important mechanism in a democratic country. The subject matter in this research is to see the performance of the Election Commission of Southwest Sumba Regency as a government partner in organizing and facilititating the political competition in Southwest Sumba Regency for the sake of channeling the right to vote for the people as resident in the unitary state of the Republic of Indonesia. The purpose of this study is to determine the performance of the Election Commision of Sothwets Sumba Regency in the implementation of regional elections of southwest Sumba Regency in 2013. This research method is a descriptive method as a method of solving problem studied based on the facts. The method of collecting data uses interview, observation and documentation techniques. Data analysis is done by interactive data analysis tecniques from Milles and Huberman. The result of the study show that the performance of the Election Commision of Southwest Sumba Regency in holding regional head elections in Southwes Sumba Regency in 2013 was categorized as quited good. This can be seen from several indicators that are used as a measure by researcher in this study, including a) determination of the final voter list, b) socialization, c) voting, d) vote counting, e) determination and announcement of election result, f) handling findings of cases in the election of regional heads in Southest Sumba Regency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".