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Record W2101282445 · doi:10.1061/9780784412329.038

Exploring the Impact of WorkFace Planning on Labour Productivity Variance Mitigation on Industrial Construction Projects: A Mathematical Framework

2012· article· en· W2101282445 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueConstruction Research Congress 2012 · 2012
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsNatural Resources CanadaNatural Sciences and Engineering Research Council of CanadaEmissions Reduction AlbertaCanadian Natural ResourcesUniversity of Alberta
Fundersnot available
KeywordsProductivityProcess (computing)Variance (accounting)Computer scienceBusinessEconomicsEconomic growthAccounting

Abstract

fetched live from OpenAlex

WorkFace Planning is a construction planning and execution process developed by the Construction Owners Association of Alberta to enhance construction projects in terms of cost and productivity performance. To evaluate its effectiveness on labour productivity, a study is being conducted with a major Canadian industrial construction group that uses WorkFace Planning. In order to reliably establish a relationship between the planning process and improved project performance, a mathematical formulation is needed to capture the effects of WorkFace Planning on labour productivity. This paper presents a mathematical framework that quantifies the changes in both labour productivity and inproject costs that come from mitigation strategies developed through WorkFace Planning. The framework will be used in a research study that examines the proposed connection between WorkFace planning and improved project performance in depth.

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.

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)0.011
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.002
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.519
GPT teacher head0.488
Teacher spread0.031 · 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