Using mass timber in multi-storey and non-residential construction projects: Motivations and barriers for professionals in Quebec
Bibliographic record
Abstract
Despite their environmental benefits and technical viability, mass timber structures adoption remains limited. As an alternative to steel and concrete in non-residential and multi-storey construction, they represent only 10.2% of buildings four or fewer storeys high, 1% of those five or six storeys high and 4% of those seven to twelve storeys high in Quebec. Based on a purposive sample of 42 interviews with various construction industry professionals in Quebec (Canada), the representation of mass timber construction was highlighted. A thematic analysis approach enabled a study of the motivations and barriers to adopting mass timber and the specific reasons behind them, and to determine whether respondents’ perceptions differ significantly depending on their main professional activity. The results corroborate existing literature while offering deeper insights into motivations and barriers, revealing new viewpoints. Respondents cited construction costs, expertise, manufacturing capacity, regulatory limits, and material specifications as the most critical barriers, while environmental impact and aesthetics of wood as key motivators. The response profile analysis suggests that private developers and general contractors should be the primary targets of measures promoting mass timber adoption. This research will aid in refining policies and strategies to encourage the widespread adoption of mass timber in construction practices.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".