Social Representations of Mass Timber and Prefabricated Light-Frame Wood Construction for Multi-Story Housing: The Vision of Users in Quebec
Bibliographic record
Abstract
The increased use of wood and prefabrication are solutions that are helping to address current and future challenges in the construction sector. However, these practices are slow to become widespread due to the conservative nature of this industry. The objective of this study is to characterize the social representations of Province of Quebec (Canada) individuals with regard to these construction methods in order to determine the motivations and barriers to their use in the construction of multi-story housing. The social representations of a representative panel of the Quebec population were collected through standardized surveys. The data were analyzed using descriptive statistics. The results indicate that the use of wood in the construction of high-rise multi-story housing is not part of the representations of a majority of Quebecers. The motivations identified are the aesthetics of wood and the environmental aspect, although forestry exploitation remains an important concern for respondents. The main barriers are the safety aspect and lifespan because they tend to dominate the other themes in decision making. The results suggest that the acceptance and adoption of these construction methods by users can be improved by adapting the transfer of knowledge towards the negatively perceived themes and the identified social groups.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".