MétaCan
Menu
Back to cohort
Record W4224252821 · doi:10.1016/j.dibe.2022.100074

Error aversion or management? Exploring the impact of culture at the sharp-end of production in a mega-project

2022· article· en· W4224252821 on OpenAlex
Jane Matthews, Peter E.D. Love, Lavagnon A. Ika, Weili Fang

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

VenueDevelopments in the Built Environment · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsWilfrid Laurier UniversityUniversity of Ottawa
Fundersnot available
KeywordsReworkWorkforceAllianceProduction (economics)Rank (graph theory)Exploratory researchBusinessComputer scienceOperations managementOperations researchMarketingEconomicsSociologyMicroeconomicsPolitical scienceEngineeringMathematicsEconomic growthSocial science

Abstract

fetched live from OpenAlex

The research we present in this paper addresses the following question: What type of error culture does the rank-and-file workforce experience during construction, and does it help mitigate rework? We undertake an exploratory case study of an Alliance, which forms part of a transport mega-project. An error culture questionnaire is administered to the Alliance's subcontractors' workforce across four projects. We find that an error management culture positively correlates with reductions in rework and holds a divergent relationship with an error aversion culture. We further reveal a negative association between an error aversion culture and the ability to reduce rework. Consequently, we question the contemporary wisdom that assumes that error prevention should be combined with error management to create an adaptive culture, aiming to minimise the negative and maximise positive error consequences. We finally discuss the study's limitations and implications for future research examining error culture in 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.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.407
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.153
GPT teacher head0.360
Teacher spread0.207 · 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