Bibliographic record
Abstract
This study examines the level of acceptance of work automation among the operational officers in Work Force Deparment of Malaysia (WFD). The objectives of this study is to investigate; i) The differences of the level of acceptance of work automation among the assessment officers based on demographic factors. ii) The relationship between the assessment officer's attitude with the level of acceptance of work automation, iii) The relationship between the assessment officers skill with the level of acceptance of work automation, iv) the relationship between the assessment officers' training with the level of acceptance of work automation, v) the relationships between the tops administration with the level of acceptance of work automation. A set of questionnaires containing 39 items using questions developed from 155 operational officers from Selangor, Wilayah Persekutuan, Negeri Sembilan, Kelantan dan Terengganu. Five hypotheses were constructed for this study. Statistical analysis used include frequency, mean, median, mode , standard deviation, t-Test, One-way Analysis of Variance and different level of the acceptance of indicates that; i) There are different level of acceptance of work automation among operational officers's base on demographics factors such as group of post, ages, and attend training/programme with exception on gender, academic qualification, tenure and having personal computer (PC). ii) Significant relationships between assessment officers attitude with the level of acceptance of work automation, iii) Significant relationships between assessment officers' in job training with the level of acceptance of work automation, v) not significant relationships between top management with the level of acceptance of work automation.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".