Information technology and collaboration in the canadian construction industry
Bibliographic record
Abstract
Information plays an essential role in construction because it specifies either the resulting product (design information) or the activities that need to be carried out in order for the product to be constructed (management information). The new information technology is impacting how this information is exchanged and how organizations operate because its purpose is to facilitate the exchange and management of information and it holds a lot of potential for the fragmented construction industry. To better understand this impact, a survey was carried out in Canada to inquire about communication, efficiency, and IT usage. Its focus is on collaboration among the various stakeholders. Collaboration implies that people with at least one common goal interact (through efficient communication), share (through generous cooperation) and coordinate themselves (through synchronicity). Little is known about the technologies used and how they impact collaboration. How do stakeholders interact? What information is shared? How does coordination occur between firms and within firms across functional lines? This exploratory study aims at providing insight into these questions. The authors created a questionnaire designed to assess issues related to information technology usages. The questionnaire has eight parts: socio-demographic questions to identify the context of the respondent and his/her firm; questions on IT usage which listed 17 different communication methods or devices (from fax to web portals and groupware) and evaluated their usage frequency as well as their perceived efficiency; questions on preferred IT use requested the most efficient technology or communication modality for contacting specific stakeholders; questions on the frequency of electronic transfer of key documents; questions to assess the perceived performance of the individual 1 Professor, Psychology Dept., Universite de Montreal, 90, ave. Vincent-d’indy, Montreal, Canada, H2V 2S9, Phone +1 514/343-6498, FAX + 1 514/343-2285, f.chiocchio@umontreal,ca Correspondance regarding this paper may be sent to Francois Chiocchio at f.chiocchio@umontreal.ca. This paper was made possible by a research grant from the FQRSC. 2 Doctoral Student, Psychology Dept., Universite de Montreal, 90, ave. Vincent-d’indy, Montreal, Canada, H2V 2S9, Phone +1 514/343-6498, FAX + 1 514/343-2285, caroline.lacasse@umontreal,ca 3 Professor, Construction Engrg. Dept., ETS, 1100 Notre-Dame St West, Montreal, Canada, H3C 1K3, Phone +1 514/396-8667, FAX +1 514/396-8584, hugues.rivard@etsmtl.ca 4 Professor, Construction Engrg. Dept., ETS, 1100 Notre-Dame St West, Montreal, Canada, H3C 1K3, Phone +1 514/396-8668, FAX +1 514/396-8584, daniel.forgues@etsmtl.ca 5 Professor, Construction Engrg. Dept., ETS, 1100 Notre-Dame St West, Montreal, Canada, H3C 1K3, Phone +1 514/396-8829, FAX +1 514/396-8525, claude.bedard@etsmtl.ca June 14-16, 2006 Montreal, Canada Joint International Conference on Computing and Decision Making in Civil and Building Engineering
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".