Scaling Off-Site Construction: A Roadmap for Sustainable and Affordable Housing Solutions at a Regional Level
Bibliographic record
Abstract
This research project explores the supply side of housing shortages and the potential to increase capacity through the adoption of off-site construction (OSC) in Newfoundland and Labrador (NL).By engaging stakeholders across government, industry, and academia, the Province's potential to scale factory-built housing as a sustainable, fast and affordable solution is assessed.The approach and results were enhanced by employing similar methods and comparing the detailed outputs of recent initiatives undertaken in the United Kingdom, Australia, New Zealand and most recently in Canada.Key activities included literature reviews, workshops, and focus groups to gather insights on market demand, barriers to adoption, and opportunities for growth.The project identified challenges related to regulatory frameworks, financing and insurance, skilled labour shortages, and logistics that currently hinder the widespread use of OSC that are specific to a region.A primary outcome of the project is to contribute to an OSC housing strategy for Atlantic Canada.The results align with national housing strategies and supports local efforts to increase housing supply, particularly through multi-unit and medium-density housing.Additionally, the project provides guidance on the creation of the Atlantic Offsite Housing Innovation Network-a collaboration to foster ongoing partnerships and knowledge-sharing among stakeholders to advance offsite construction and focus on promoting low-carbon and whole-life-cycle construction practices.Ultimately, the project will contribute to a framework for sustainable, resilient housing growth in Atlantic Canada, supporting regional strategic housing goals and contributing to national housing objectives and enable benchmarking against international efforts.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".