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Record W2266839731

Using Interactive Workspaces for Team Design Project Meetings

2007· article· en· W2266839731 on OpenAlexaff
Mohamed Issa, Jeff H. Rankin, John T. Christian, Evan Pemberton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsWorkspaceContext (archaeology)Quality (philosophy)Work (physics)SustainabilityProject teamKnowledge managementComputer scienceEngineering managementEngineering
DOInot available

Abstract

fetched live from OpenAlex

An Interactive Collaboration Laboratory (ICL) has been established at the University of New Brunswick (UNB) to research the application of interactive information and communication environments for the architectural, engineering, and construction (AEC) industry. This paper provides a quick overview of the laboratory within the wider context of interactive collaborative workspaces. It identifies opportunities to enhance information communication, and group decision-making offered by the laboratory, and focuses on lessons learned to date from its use. The paper reports on a survey conducted among final year undergraduate students who used the environment over the course of three months for their senior design project meetings. A questionnaire was distributed to those students to investigate the impact of the environment upon the effectiveness of their meetings and decisions, the issues and processes where the environment was more (or less) useful, and the context within which the environment and tools were used. The questionnaire also investigated the impact of the environment and its tools upon their project, the quality of their work, and their overall satisfaction. Students found the laboratory to be specifically useful at the preliminary design stage when designing, viewing, and analyzing the site and building layouts of their projects, and determining the project’s sustainability requirements, and targets. The laboratory enabled student groups to view information from different perspectives, access remote information, and save captured information instantaneously. It also enabled them to collaborate more effectively, make more educated decisions, make better use of their time, produce higher quality work, and develop among them a relationship of trust, respect and mutual understanding. Investigating how best to use the lab’s technology to serve their needs, occasionally slowed down their progress and distracted them at times from focusing on their work.

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)0.012
metaresearch head score (Gemma)0.024
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 score0.012
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.001
Scholarly communication0.0050.005
Open science0.0030.008
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.003

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 head0.041
GPT teacher head0.294
Teacher spread0.253 · 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

Citations8
Published2007
Admission routes1
Has abstractyes

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