A SURVEY ON THE IMPACT OF INFORMATION TECHNOLOGY ON THE CANADIAN ARCHITECTURE, ENGINEERING AND CONSTRUCTION INDUSTRY
Bibliographic record
Abstract
SUMMARY: A survey about the current and planned use of information technology (IT) and its impact on the architecture, engineering, and construction (AEC) industry in Canada has been conducted at the end of 1998 and beginning of 1999. It was found that many business processes are now almost completely computerised and the tendency is toward a greater computerisation of the remaining processes. Although the Internet has been adopted by most firms surveyed, design information is still exchanged in its traditional form. These firms have increased and will increase further their investment in IT, which has raised productivity in most business processes and has resulted in an increase in the quality of documents and in the speed of work, better financial controls and communications, and simpler access to common data. However, the benefits of IT come at a cost since the complexity of work, the administrative needs and the costs of doing business have all increased. The continual demand for upgrading and the greater know-how required are considered important obstacles. The two most important areas of future research is the implementation of computer-integrated design and construction as well as the development of new tools to support concurrent design and to assist designers in the conceptual stages.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".