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Record W1979987888 · doi:10.1080/0144619032000111232

Innovation in clean-room construction: a case study of co-operation between firms

2003· article· en· W1979987888 on OpenAlexaff
Rob Shields, Kevin R. West

Bibliographic record

VenueConstruction Management and Economics · 2003
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsCarleton University
Fundersnot available
KeywordsLiminalityMultinational corporationNegotiationBusinessProcess (computing)Function (biology)Construct (python library)Flexibility (engineering)WorkspaceIndustrial organizationKnowledge managementManagementFinanceSociologyEconomicsComputer science

Abstract

fetched live from OpenAlex

This study examines partnering between a large client, multinational contractors and specialist suppliers, and local subcontractors involved in a project to construct clean room facilities. An ethnographic approach is used, which demonstrates the changing attitudes, values and the new working arrangements that emerged. The social bond of a ‘construction challenge’ was the basis of trust and sharing risk in a ‘quasi-fixed network’. In place of formal contracts, ongoing bargaining and continuous negotiation took place. However, the client was seen to dominate the construction process. Shared workspaces or ‘liminal zones’, betwixt and between firms, were created to allow collaboration. These are argued to be a practical organizational approach to sharing information and co-ordinating inter-firm activities. Among trades, agreements were struck to exchange training and apprenticeships for allowing foreign specialists and equipment to be imported by the high-purity gas supplier. Liminal zones appear to function as on-the-job classrooms for rapidly training workers in unfamiliar construction techniques and systems.

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)0.010
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.016
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0150.008
Scholarly communication0.0070.004
Open science0.0030.007
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.092
GPT teacher head0.337
Teacher spread0.244 · 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 designQualitative
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

Citations20
Published2003
Admission routes1
Has abstractyes

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