MétaCan
Menu
Back to cohort
Record W4296079369 · doi:10.29173/mocs278

A principal component analysis of Organisational BIM Implementation

2022· article· en· W4296079369 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2022
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
Fundersnot available
KeywordsNonprobability samplingBuilding information modelingStatus quoProcess managementYardstickProcess (computing)Knowledge managementPrincipal (computer security)Construction industryBusinessCompetitive advantageComputer scienceOperations managementEngineeringConstruction engineeringMarketing

Abstract

fetched live from OpenAlex

BIM implementation by organisations is a bit challenging for many organisations. It has become an essential yardstick for project execution in the construction industry. However, many organisations struggle to achieve its implementation as they are still in the chaotic stage due to the BIM introduction. However, the knowledge of the inherent value and usefulness resulting from BIM implementation can help them transform from the status quo to a new status quo. The study adopted purposive sampling through a quantitative approach to identify the merits of organisational BIM adoption. Data was collected using a structured questionnaire from thirty BIM aligned construction organisations. The study identified the critical BIM benefits to construction organisations. In addition, the structure among the factors was identified through principal cluster analysis and three clusters were identified; these are achieving competitive advantage through BIM adoption, effective organisational process and enhanced work output and achieving project outcome. The results of this study provide insight, and it is instructive to stakeholders in the construction industry to aid BIM diffusion.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.210
Teacher spread0.203 · 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