Best Practices in Building Systems (BPiBS): Advancing Knowledge Mobilization through Road Mapping
Bibliographic record
Abstract
The Best Practices in Building Systems (BPiBS) initiative seeks to drive much-needed change in the housing sector and building industry by addressing pressing technical, economic, environmental, social and health challenges.Through collaborative research and stakeholder engagement, BPiBS is developing an iterative roadmap to enhance building systems for new and existing housing across urban and rural Canada, beginning in British Columbia and informing national solutions.This paper outlines the project's vision, focus areas, and early progress.Thematic research includes energy systems, building exteriors and grounds, health-centric design, water and waste systems, and building forms and structures.Guided by the objectives of economic value, environmental sustainability, health and wellbeing, social equity, and innovation, BPiBS advances evolving, context-responsive best practices.These approaches are shaped by continuous learning across technical, ecological, economic, and social domains.They balance scalability with specificity, aiming for long-term well-being while addressing short-term needs.Key activities include stakeholder engagement, a structured knowledge repository, use of AI and machine learning for interdisciplinary data analysis, scenario planning, and formulation of a roadmap and strategy for knowledge mobilization and capacity-building.A graph database underpins the computational platform, organizing data into a living roadmap informed by input from policy actors, industry leaders, and educators.By enhancing alignment across industry, regulatory and education spheres, this project streamlines decision-making and accelerates adoption of emergent best practices within building systems.These systems are integrated networks of structural, infrastructural, and socio-environmental elements that support the function, adaptation, and livability of built environments.In housing, they co-create the places we call home.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".