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Innovation in Construction: A new norm against Traditional Construction

2022· article· en· W4320802102 on OpenAlexaff
Muhammad Ali Musarat, Muhammad Altaf, Muhammad Babar Ali Rabbani, Abdullah O. Baarimah, Wesam Salah Alaloul, Khalid Mhmoud Alzubi

Bibliographic record

Venue2022 International Conference on Data Analytics for Business and Industry (ICDABI) · 2022
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsConstruction industryScopusNorm (philosophy)Knowledge managementComputer scienceConstruction engineeringEngineering managementEngineeringBusinessPolitical science

Abstract

fetched live from OpenAlex

The construction industry is demonstrating expansion with time where the position of innovation in construction is significant. This paper performs a systematic review under the guidelines of the PRISMA statement where the Scopus database was chosen to extract the relevant articles based on the selective keywords. The output shows 67 articles that were reduced to 17 for a full review after applying the limitations. The final chosen articles revealed the reforms in the construction sector after the inclusion of innovation in construction, mostly the digitalization of the industry. This review will help the construction industry stakeholders to get the inside of the construction innovation and recommended to educate the construction industry stakeholders so that after learning they can easily apply innovation techniques in the construction projects.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.067
metaresearch head score (Gemma)0.087
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.067
Threshold uncertainty score0.355

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0670.087
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0110.013
Science and technology studies0.0030.035
Scholarly communication0.0220.023
Open science0.0020.009
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0030.001

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 head0.115
GPT teacher head0.277
Teacher spread0.162 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2022
Admission routes1
Has abstractyes

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