Citizen views on wood as a construction material: results from seven European countries
Bibliographic record
Abstract
Multi-story wooden buildings are hailed as a favorable means toward reducing the embodied energy of the construction sector. However, the sector’s path-dependent nature hinders acceptance of using wood in multi-story construction. As a result, research predominantly focuses on examining the perceptions of construction professionals to identify means of breaking the path dependency. We propose using citizens’ perceptions about the use of wood to inform professional decision makers. Our research thus aims to answer two questions: What are citizens’ perceptions about using wood as a construction material, and are there country-based cultural differences between these perceptions? To elicit this spectrum of citizen views, an online survey was deployed in Austria, Denmark, Finland, Germany, Norway, Sweden, and the United Kingdom. Qualitative content analysis was used to analyze 6633 open-ended responses to the survey. Respondents held multi-faceted opinions about the physical properties, environmental, social, and economic aspects of using wood as a construction material. Citizens from Finland, Norway, and Sweden expressed discernably different perspectives about the acceptability of using wood than did citizens from Austria, Denmark, Germany, and the United Kingdom. Overall, respondents from all countries expressed high approval for the use of wood in construction.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".