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Record W2232643802 · doi:10.5539/ass.v12n1p129

Assessing the Duties and Competencies of Female Quantity Surveyors

2015· article· en· W2232643802 on OpenAlexvenueno aff
Mastura Jaafar, Jalali Alireza, Nurhafifah Mohd Sini

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsProcurementActivity-based costingBusinessNegotiationTeamworkControl (management)Work (physics)QuestionnaireAccountingCall for bidsCore competencyConstruction industryOperations managementMarketingManagementEngineeringPolitical scienceEconomicsSociologyConstruction engineering

Abstract

fetched live from OpenAlex

<p>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.</p>

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.003
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.259
GPT teacher head0.444
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2015
Admission routes1
Has abstractyes

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