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Record W1992935712 · doi:10.1080/15578770902952280

The Effects of Prevailing Wage Regulations on Construction Efficiency in British Columbia

2009· article· en· W1992935712 on OpenAlexaboutno aff
Kevin Duncan, Peter Philips, Mark J. Prus

Bibliographic record

VenueInternational Journal of Construction Education and Research · 2009
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationEfficiency wageWageMinimum wageEconomicsSample (material)FrontierConstruction industryTerm (time)Labour economicsEngineeringPolitical scienceLaw

Abstract

fetched live from OpenAlex

This study examines the effect of prevailing wage legislation on the efficiency of school construction. Specifically, stochastic frontier regression and British Columbian construction data are used to measure the effect of the introduction and expansion of prevailing wage requirements on technical efficiency, or the ability of builders to obtain maximum output from available resources. Results indicate that average technical efficiency for all construction projects in the sample is 94.6%. Average efficiency for projects covered by the introductory stage of British Columbia's construction wage legislation is 86.6%. By the time of the expansion of the wage policy 17 months later, the average efficiency of covered projects increased to 99.8%. These findings suggest that the introduction of prevailing wage laws disrupted construction efficiency. However, in a relatively short period, the construction industry adjusted to wage requirements by increasing overall efficiency. A short-term decrease in construction efficiency, followed by a sharp and durable increase, supports that view that prevailing wage laws are not associated with higher, long-term construction costs.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.937
Threshold uncertainty score0.787

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.039
GPT teacher head0.428
Teacher spread0.388 · 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

Citations9
Published2009
Admission routes1
Has abstractyes

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