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Streamlining the Construction Productivity Improvement Process with the Proposed Role of a Construction Productivity Improvement Officer

2011· article· en· W2028931726 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.

Bibliographic record

VenueJournal of Construction Engineering and Management · 2011
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsProductivityContext (archaeology)Competitive advantageConstruction industryProcess (computing)Work (physics)BusinessIndustrial organizationPosition (finance)OfficerEngineeringProcess managementOperations managementMarketingComputer scienceConstruction engineeringEconomicsEconomic growthGeographyFinance

Abstract

fetched live from OpenAlex

Construction productivity improvement has become a key area of focus among academia and industry over the last decade attributable to its strong potential in benefitting the construction industry. Despite its high impact on the construction industry, productivity improvement is still an area in which much research work needs to be done to explore its true potential in a practical industry context. Today’s construction industry seems to adopt productivity improvement initiatives to gain a competitive edge in the global market place; however, systematization of these approaches is still an area of concern. This paper discusses a framework for the implementation of productivity improvement activities on a construction site, making the process more systematic, accountable, and sustainable with the creation of the construction productivity improvement officer (CPIO), a dedicated position, on construction sites.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.958
Threshold uncertainty score0.716

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.017
GPT teacher head0.244
Teacher spread0.227 · 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