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Record W4383682266 · doi:10.35490/ec3.2023.234

Barriers to circularity in construction: an analysis of experts’ perspectives

2023· article· en· W4383682266 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

VenueComputing in construction · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsConcordia University
Fundersnot available
KeywordsCategorizationDemolitionDeconstruction (building)Computer scienceProcess managementAction (physics)Knowledge managementData scienceRisk analysis (engineering)Management scienceEngineeringBusinessArtificial intelligenceCivil engineering

Abstract

fetched live from OpenAlex

Adopting Circularity in Construction (CiC) is complicated and requires the transition from demolition to deconstruction of the built environment. Although industry practitioners have mentioned the barriers to the implementation of CiC several times, its application remains in its infancy stage. This study aims to identify and classify the barriers to implementing CiC in a comprehensive yet straightforward way. A three-stage methodology was adopted: data collection, content analysis, as well as barriers analysis and categorization. A total of 37 consolidated barriers were identified and grouped into six categories that could assist in developing action plans and effective strategies to adopt CiC.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.014
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.082
GPT teacher head0.397
Teacher spread0.316 · 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