Penerapan Metode Preference Selection Index (PSI) Penentuan Penilaian Kinerja Fasilitator di BBPPMPV BBL Medan
Bibliographic record
Abstract
Organizational performance includes various indicators such as productivity, work quality, efficiency, and innovation. Challenges often arise in the performance evaluation process when not supported by an adequate system. Additionally, facilitators play a crucial role in achieving goals, but poor selection of facilitators can lead to problems like lack of participation, poor time management, and inability to resolve conflicts, negatively impacting productivity and decision quality. This study aims to develop a support system that improves performance evaluation and facilitator roles at BBPPMPV-BBL Medan. The system is expected to simplify performance evaluation, enhance resource management effectiveness, and assist facilitators in carrying out their tasks optimally. The research findings indicate that this support system provides more accurate, structured, and effective performance evaluation, while enhancing facilitators' ability to maintain focus, manage time, and resolve conflicts. Implementing this system at BBPPMPV-BBL Medan contributes to increased productivity, efficiency, and performance quality, and enables proper recognition of high-performing individuals or teams.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".