Employability of local trained skill labour in construction industry
Bibliographic record
Abstract
Development in Malaysia is booming which can be witnessed by the various construction projects that currently in progress, especially in the state of Johor which has the highest value of construction work completed for the third quarter of 2016. This necessarily requires skilled labours in a high number especially among the locals since it has been reported that Malaysia’s construction industry is having problems related to the shortage of local skilled labour. In addition, the local workers have been reported to unable to fulfil the demand of construction market and this have caused the contractor to import foreign workers to meet the needs and requirement of labour market in construction sector. The objectives of this study are to determine the criteria set by the construction company in recruiting local skilled labour and to study the strategies that can attract local skilled labour to join construction industry. Questionnaire has been distributed to G7 contractor in the state of Johor in order to achieve the objectives of this study. Collected data was then evaluated and tested for its reliability using the SPSS 20.0 software before it can be analysed in order to obtain the mean value, frequencies and percentage. The outcome of this study indicates that the prospective employer prefers to work with man and they require young, experienced, knowledgeable and skilled workers in doing the job. Most of the strategies that have been selected are mainly related to money namely salary increment, bonus, allowance and overtime payment, apart from upgrading labours welfare and providing a better accommodation. This study can be a guideline to both skills institution and contractor to improve on what they are lacking in order to encourage the local trained skills labour to join the industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".