Development of Smart Human Resource Planning System within Rajabhat University
Bibliographic record
Abstract
The purposes of this study were to 1) develop of Smart Human Resource Planning System within Rajabhat Universities and 2) study the results of official performance evaluations of academic staff with Smart Human Resource Planning System within Rajabhat Universities. The samples included 8 system development experts via purposive sampling and 94 academic staff by multi-stage sampling. The research tools composed of 1) performance assessment form using 5-point Likert scale for Smart Human Resource Planning within Rajabhat Universities and 2) performance evaluation form for academic staff with Smart Human Resource Planning System within Rajabhat University. The research observations were concluded into 2 ways. First, the Smart Human Resource Planning System within Rajabhat Universities development has overall performance at the high level. For instance, the efficiency of all Modula test was displayed at the high level. In addition, both System test, Usability test and Security test were shown at high level as well. Second, the response of performance evaluation form through academic staff using Smart Human Resource Planning System was all exhibited at high level. However, “The people involved with the system” assessment list with in performance evaluation form was indicated at highest level.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".