Enhancing the performance and productivity of the New Brunswick construction industry through appropriate digital technology adoption : final report
Bibliographic record
Abstract
This report contains the results of a study for the New Brunswick Construction Industry under the project entitled “Enhancing the performance and productivity of the New Brunswick construction industry through appropriate digital technology adoption.” The project is supported by the Digital Technology Adoption Pilot Program (DTAPP), delivered by the National Research Council of Canada Industrial Research Assistance Program (NRC IRAP). The study was completed by the University of New Brunswick Construction Engineering and Management (UNB CEM) Group over the period of August 2012 – March 2013. The researchers that undertook the work were Jeff Rankin (UNB CEM), Lloyd Waugh (UNB CEM), and Dhirendra Shukla (UNB Technology Management and Entrepreneurship). The general purpose of the study was to develop tools to assist the construction industry in the successful adoption and implementation of new technologies. The study was accomplished by completion of the following steps: • A framework was developed for the assessment of management practices at the project level for general contractors in the construction industry. • The assessment was administered to eight organizations, resulting in the identification of potential opportunities for improvement. • Opportunities for improvement were validated with six organizations. • The assessments were aggregated to provide an initial benchmark of the level of implementation of management practices. The results are summarized as follows: • Five organizations have indicated a desire to further pursue adoption and implementation projects to improve their management practices. • At an industry level, management practices in need of improvement include: managing safety information; developing schedules; managing materials on-site, and capturing the impact of rework. Future steps are to assist organizations in their pursuit of adoption and implementation projects; and collect additional data to expand the usefulness of the industry benchmarks for management practices.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".