Human resources development assessment planning program and bureaucratic reform management on the performance of government organization
Bibliographic record
Abstract
The purpose of this study is to analyze the assessment of Human Resources Development (HRD) Planning on the Government Organization Performance through Bureaucratic Reform Management. The Government Organization tasked with preparing public goods/services must be able to provide certainty of their performance capacity as an organization that is professionally organized and is non-excludability in providing an adequate level of service. This research is based on the performance of Government Organization that have not been maximized. The method used is the Second Order Structural Equation Modeling analysis method. The research results showed that HRD Planning had a significant influence on Organization performance through Bureaucratic Reform Management. Tests on the research model simultaneously proved that the model was fit with the fulfillment of all model fitting sizes indicated by the value of GFI = 0.925, CFI = 0.927, RMSEA = 0.075, and CMIN / DF = 1.995. The findings of this study prove that HRD Planning has a significant effect on Organization performance through Bureaucratic Reform Management. Based on these findings, the right strategy to strengthen Organization performance can be done by improving aspects of HRD Planning. Also, there is a need to pay attention to the management of strategic change by being more responsive and adaptive to environmental changes and HRD Planning.
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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 | 0.005 | 0.020 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".