Bibliographic record
Abstract
In the recent past, there has been a proliferation of activities both in research as well as commercial arenas relating to Information and Communications Technologies (ICT) usage in the construction industry. However, it is not easy to gauge the extent to which success has been achieved or even the extent to which the industry as a whole might be exploiting the ICTs. One way of gauging this is obviously through detailed surveys. This paper considers some such surveys from the two sides of the Atlantic (specifically USA and UK) and attempts to draw conclusions from them. The acronym, SITIES, could metaphorically relate to the two`cities', USA and UK, as well as Surveys of IT In architecture, Engineering and conStruction. The data from USA is largely drawn from CFMA (Construction Financial Managers Association) Bi-Annual IT Surveys. The data from the UK is drawn from various sources including CICA (Construction Industry Computing Association), BRE (Building Research Establishment) and ITCBP (Information Technology Construction Best Practice). The surveys from the two sides of the Atlantic are quite dissimilar in nature and, thus, make it hard to make comparisons between them. However, it is still possible to make some interesting observations about the nature of ICT exploitation in the two countries. The paper concludes that technologies being used in both contexts are quite similar but there are differences in certain trends in terms of uptake of these technologies. Besides, the trade organisations in the USA seem to be more actively involved in encouraging and supporting activities in this area for their members than the UK.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.047 | 0.012 |
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".