Building material preferences with a focus on wood in urban housing: durability and environmental impacts
Bibliographic record
Abstract
As societies urbanize, a growing proportion of the global population and an increasing number of housing units will be needed in urban areas. High-rise buildings and environmentally friendly, renewable materials must play important roles in sustainable urban development. To achieve this, it is imperative that policy makers, planners, architects, and construction companies understand consumer preferences. We use data from urban dwellers in the Oslo region of Norway to develop an understanding of material preferences in relation to environmental attitudes and knowledge about wood. We emphasise wood compared with other building materials in various applications (structural, exterior, and interior) within urban apartment blocks. We use 503 responses from a web panel. Our findings show that Oslo area consumers tend to prefer materials other than wood in various applications in apartment blocks, especially structural applications. Still, some respondent prefer wood, including some applications in apartment blocks where wood is currently not commonly used. The best target for wood-based urban housing includes younger people who have strong environmental values. As environmental attitudes evolve in society and a greater proportion of consumers search out environmentally friendly product alternatives, the opportunities for wood to gain market share will most likely increase.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".