From Projects to Systems: the Alberta Facilities Production System (AFPS)
Bibliographic record
Abstract
The integration of academia-industry collaboration has a crucial role in advancing innovation and improving efficiency in the construction industry.Most of these collaborations remain fragmented, with academia pursuing theoretical research while industry focuses on practical business first execution.This is one of the obstacles to making Lean implementations truly effective, since continuous learning, adaptation, and system-wide integration are essential for such implementations.Although the benefits of Lean production systems are well-documented, construction projects tend to adopt isolated Lean tools on an individual project basis, lacking a structured framework for sustained implementation across many projects.This paper introduces the Alberta Facilities Production System (AFPS), a conceptual framework that bridges the academia-industry gap through the integration of Lean principles into large-scale public sector construction.Inspired by the Toyota Production System (TPS), AFPS ensures that academic research reinforces industry practice, while industry challenges and experiences drive academic research.The University of Alberta serves as a "Live Lab" to test, refine, and scale Lean strategies, in order to eventually optimize construction processes.By establishing a structured and data-driven partnership between academia and industry, AFPS aims to promote continuous improvement, waste reduction, and knowledge transfer.This paper contributes a roadmap that will help governments, universities, and industry practitioners collaborate effectively and efficiently.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".