CASE STUDIES ON THE USE OF INFORMATION TECHNOLOGY IN THE CANADIAN CONSTRUCTION INDUSTRY
Bibliographic record
Abstract
A series of eleven case studies were gathered from across Canada in the summer of 2002. These case studies define an initial compendium of Best Practice in the use of information technology (IT) in Canada. The professionals interviewed included architects, engineers, general contractors, and owners. Many of them are at the cutting edge in their use of IT. The documentation of their pioneering use of IT can demonstrate how useful these technologies can be and what potential pitfalls are of concern. The case studies cover architecture, engineering, construction management, and specialized contractors. The following technologies were demonstrated: 3D CAD; custom Web sites; commercial Web portals; and in-house software development. No case was found that used wireless communication or standardized data formats such as IFCs or CIMSteel. The following issues were identified: the electronic distribution of documents is more efficient and cheaper; the short time-line and the tight budgets make it difficult to introduce new technologies on projects; the industry is locked in one CAD system and it is difficult to introduce new ones; it is costly to maintain trained CAD and IT personnel; and companies that lag behind reduce the potential benefits of IT. Still, the industry could achieve substantial benefits from the adoption of IT if it would be more widespread.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".