The University of Alberta’s Construction Innovation Centre (CIC): Academia-Industry Collaboration for High-Impact Research
Bibliographic record
Abstract
The Construction Innovation Centre (CIC) was established at the University of Alberta with the aim of providing breakthrough research, education, and training that would directly benefit the construction industry, leading to sustainable and economic development of our built environment—all while providing a competitive advantage for the Canadian construction sector. Bringing together over 30 inter-disciplinary faculty members and more than 50 industrial partners, professional associations, and government bodies, the CIC is accelerating and supporting innovation, productivity, and competitiveness in the construction industry through high-impact research. This paper shares the steps taken to establish the CIC including the development of its research road map, governing structure, services provided, and metrics to evaluate performance. By exploring the process of establishing the CIC as a collaborative construction research center, this paper provides insights into the opportunities, challenges, and directions for creating successful academia-industry collaborations. Such collaborations are crucial for developing impactful solutions that can address the challenges encountered by the construction industry through cultivating a collaborative environment for effective innovation and knowledge mobilization, successful dissemination of research findings, and productive training of highly qualified personnel.
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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.016 | 0.003 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".