Training practices for professionals and technical staffs in selected menufacturing sector
Bibliographic record
Abstract
The objective of this study was to describe the training practices for professionals and technical staffs in the four selected industries in the manufacturing sector in Malaysia. The population frame was based on the directory of manufacturers throughout the country provided by MIDA (MIDA, 1996). Due to the large number of companies and limited budget, this study will only focus on four industries i.e the electrical and electronics industry, chemical and chemical products industry. From the 1340 companies, 60 companies were identified as respondents and were chosen by using the stratified random sampling method.Interviews were structured through the use of a form and were conducted with the appropriate staff members of the companies who are in-charge of the training activities.Findings of this study on the training objectives, the importance of training and development, influence on budget by various job categories, the training need assessment analysis, the training needs assessment data gathering methods, the training types, training methods, training providers, training venues, instructional media and the training evaluation supported findings from previous researches even though those researches did not specifically focus on the training of professionals and technical staffs in the manufacturing sector.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".