EVALUATING THE HEALTH AND SAFETY MATURITY OF SUSTAINABLE BUILDING PROJECTS USING A SUSTAINABLE HEALTH AND SAFETY MATURITY MODEL
Bibliographic record
Abstract
ABSTRACT The health and safety maturity of 20 sustainable building projects and 21 non-sustainable ones in Manitoba was evaluated using a Sustainable Health and Safety Maturity Model comprising 22 safety maturity drivers and 251 critical to safety practices assessed via a questionnaire survey. Sustainable building projects were found to have a higher level of health and safety maturity than that of the non-sustainable ones. Larger-sized companies were found to implement more mature health and safety practices on their sustainable building and non-sustainable building projects than smaller and medium sized companies. The safety maturity drivers of “safety policy and standard implementation,” “safety inspections” and “incident investigation, reporting and performance” were the most mature on sustainable building and non-sustainable building projects whereas “designing for safety,” and “alcohol and drug testing” were the least mature. General contractors can use the maturity model to evaluate and improve their projects’ health and safety maturity. Safety practitioners can also focus efforts on the safety maturity drivers with the highest influence to help enhance the effectiveness of their safety programs, especially when faced with resource constraints.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".