Pengaruh Human Capital dan Social Capital Terhadap Intelectual Capital Dengan Organizational Citizenship Behaviour sebagai Variabel Mediasi pada Perangkat Desa di Kabupaten Kerinci
Bibliographic record
Abstract
The phenomenon of development development is in rural areas. In order to realize national development goals, the government pays the greatest attention to development in the countryside. So that the spearhead of the village is one of them is the Village Device is required to improve the quality of self reflected in human capital, social capital, Intelectual capital and Organizational Citizenship Behavior (OCB). This research aims to find out the Influence of human capital and social capital on Intelectual capital Village Devices With Organizational Citizenship Behaviour as a Mediation Variable on Village Devices in Kerinci Regency. Respondents in this study were Village Devices in Kerinci Regency by taking samples of village devices located in Gunung Raya and Bukit Kerman districts so that 174 respondents were obtained. The Model Testing Technique in this study used the Stuctural Equation Model (SEM). Human capital and social capital had an effect of 98.1% on Organizational Citizenship Behavior (OCB) on Village Devices in Kerinci Regency and Human capital, social capital and Organizational Citizenship Behavior (OCB) had an effect of 15% on Intelectual Capital on Village Devices in Kerinci Regency.
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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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".