The Model of Human Resources Performance Development on the Leader Organization of Regional Devices at Salatiga Government
Bibliographic record
Abstract
The purpose of this research is to test and analyze the impact of transformational leadership and the religiosity values to the knowledge sharing and the performance of human resources. This is an explanatory research that emphasized on the relation between research variable by testing the hypothesis. To get the complete data and accurate also accountable the scientific truth used the questionnaire and interview. Instrument used in this research are questioners in Likert scale 1 to 5. Total of the respondents in this research are 141 SCAs in Salatiga Government. Researcher collects online quest data using Google form which send directly to the respondents, until the exact amount fulfilled. Data analysis in this research use Partial Least Square (PLS). Result of this research shows that Transformational Leadership has significant positive effect to the Knowledge Sharing. The Transformational Leadership has positive effect to the performance of human resources. Religiosity values have positive effect to the Knowledge Sharing but found that Religiosity Values do not have any significant effects to the performance of human resources. Knowledge Sharing doesn’t have significant effect to the improvement performance of human resources. So that the performance of human resources can be improved by implementation of Transformational Leadership. Knowledge Sharing in the organization can be improving by Transformational Leadership and Religiosity values implementation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".