Assessing the Duties and Competencies of Female Quantity Surveyors
Bibliographic record
Abstract
Quantity surveyors (QSs) in developing countries remain tied to their traditional duties. This study aims to investigate the level of duties and competencies of female QSs in the Malaysian construction industry. The research objectives are determined via a questionnaire administered to 37 construction firms around Peninsular Malaysia. In relation to the three types of competencies, female QSs possess mandatory competencies in teamwork and conduct rules, ethics, professional practice, communication, and negotiation. They also possess the core competencies of procurement and tendering, quantification and costing of construction work and project financial control and reporting and optional competencies in project evaluation, contract administration, and contract practice. Their main duties are focused on tasks related to the pre tendering, construction, and project completion stages, which involve final account, cost control, costing, and preparation of financial statements. These duties are significantly correlated with major competencies, such as procurement, quantification, costing of construction work, and project financial control. This study shows that the involvement of female QSs in the construction industry in Malaysia remains dominated by the traditional practices. Based on the profile of the respondents, this study considerably reflects the middle categories of female QSs who work in quantity surveying firms.
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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 | 0.006 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".