EFEK INTERAKSI KINERJA DAN SENSE OF HUMOR PENYULUH SOSIAL PADA PENINGKATAN PARTISIPASI MASYARAKAT DALAM PROGRAM KESEJAHTERAAN SOSIAL DI INDONESIA
Bibliographic record
Abstract
Penelitian bertujuan untuk menganalisis pengaruh kinerja, sense of humor, dan interaksi antara kinerja dan sense of humor penyuluh sosial pada partisipasi masyarakat dalam program pembangunan kesejahteraan sosial di Indonesia. Penelilitian ini menggunakan metode survey kuantitatif dan melibatkan 124 penyuluh sosial sebagai responden yang tersebar di beberapa provinsi di Indonesia. Instrumen pengumpulan data menggunakan kuisioner kinerja penyuluh sosial, multidimensional sense of humor, dan partisipasi masyarakat. Analisis data menggunakan Moderating Structural Equation Modeling (MSEM). Hasil penelitian menemukan pengaruh positif interaksi kinerja dan sense of humor penyuluh sosial pada peningkatan partisipasi masyarakat dalam program kesejahteraan sosial di Indonesia. Penelitian ini juga menemukan pengaruh positif kinerja penyuluh sosial pada partisipasi masyarakat, pengaruh positif sikap terhadap humor pada penggunaan humor coping, pengaruh positif pengembangan kualitas penyuluhan pada partisipasi masyarakat dalam perencanaan program
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".