Woodball sports development management survey at IWbA Kebumen Regency
Bibliographic record
Abstract
The purpose of this study was to determine the management of woodballsports development in the IWbA district of Kebumen Regency in 2022. The researchwas conducted at IWbA Kebumen Regency with data collection methods usingobservation, interviews, and documentation. The research instruments used wereinterview, observation, and documentation guidelines. Checking the validity of this research data by data triangulation and source triangulation. The data analysistechnique uses data reduction, data presentation and conclusion drawing. Basedon the research results obtained data that; 1) The first management function isplanning (planning) as a whole has not gone well because there is a shortage in theinfrastructure used for training, 2) The second management function is organizing(organizing) the implementation has not gone well, needs to be improved for theadministrators to become administrators who are active in supporting the smoothdevelopment of woodball for the Kebumen Regency Regional Government, 3) Thethird management function of actuating has been running well in accordance withthe program made by the coaches and administrators, 4) The fourth managementfunction is monitoring (controlling) the achievements of IWbA Kebumen Regencyalready running well, but needs to be improved again. The conclusion from theresults of this study is that the management of the woodball sports at the IWbAdistrict of Kebumen Regency in 2022 which includes planning and organizing hasnot been going well, while the mobilization and supervision have been going well.The suggestion from this research is that the management of IWbA in KebumenRegency still needs to be improved and improved, especially in terms of the patternof coaching in order to achieve better achievements in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".