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Record W2199488367 · doi:10.22260/isarc2013/0070

A Model for Implementing & Continuously Improving the Automated Change Management Process for Construction Mega Projects

2013· article· en· W2199488367 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

VenueProceedings of the ... ISARC · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicProduct Development and Customization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceProcess (computing)Project managementProcess managementQuality (philosophy)The InternetChange management (ITSM)Engineering managementKnowledge managementBusinessOperations managementEngineeringWorld Wide WebSystems engineering

Abstract

fetched live from OpenAlex

In the construction industry, project changes constitute a major cause of delay, disruption, cost increases, poor quality and unsatisfying performance. They are also known as a main factor of litigation between the owners and constructors. Construction projects are essentially process-based, and with the critical role of Information Technology (IT) in this industry, projects, especially mega projects, are being managed remotely. The volume of data and documents, such as Requests For Information (RFIs), different types of Change Requests (CRs) and Project Change Notices (PCNs), being transferred and exchanged amongst the stakeholders of a project is considerable. The circulation of these documents amongst project stakeholders still heavily relies on two methods or levels for management of change process; Conventional (fully paper-based, Faxes, Snail mails) and Electronic or semi-automated (Email, Internet, PDF files).

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.897
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
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 head0.051
GPT teacher head0.253
Teacher spread0.202 · 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