Evaluating Stakeholder Concerns with Wood Frame Buildings and Fire Risk: A matter before the Ontario legislature - Private Member's Bill 52, Ontario Forestry Revitalization Act 2012
Bibliographic record
Abstract
1.This report evaluates the key stakeholder concerns with the proposed Ontario (ON) Private Member's Bill 52 -"Ontario's Forestry Industry Revitalization Act (Height of Wood Frame Buildings), 2012" (aka Bill 52): a matter that is currently before the Ontario Legislator.2. The fundamental change to the provincial building code act that would be possible under Bill 52 would be to increase the maximum building height for wood frame buildings in ON to 6 storeys.The expected impacts, inter alia, include creation of jobs, increased availability of affordable housing, increased taxation density, and minimisation of the carbon footprint of building construction in ON.3. Several key stakeholders within ON have raised a range of concerns towards the changes implicated by Bill 52, which largely focus on concerns with respect to the safety of the wood buildings that would be permitted under these proposed changes.These concerns are summarized within the report, and are responded to with respect to a range of information, including published proposals, research findings, analysis of fire incidents data, and case studies.
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 | 0.036 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".