Trajectories of innovation in wood construction: an actor-network analysis of building decarbonization practices in Canada
Bibliographic record
Abstract
Following a trend in other Northern countries, since 2006 the Quebec Government in Canada has engaged in a program to reduce carbon emissions by promoting the adoption of innovation in wood construction. Despite today’s importance of building decarbonization, few studies have explored innovation drivers and barriers in wood construction and the impact of government initiatives. For many observers in Canada, the glass is still half empty, for others, half full. How is innovation in wood construction legitimized and achieved and with what consequences? Here, we combine principles of Actor-Network Theory and Socio-Technical Systems to analyze interactions between stakeholders involved in the development and adoption of laminated wood construction systems in Quebec. Results illustrate the complexity of innovation processes and networks at the intersection between public funding, a fragmented construction industry, a fragile real estate sector, and heterogeneous forestry and manufacturing practices. In a context where environmental narratives compete, justifications based on the need for climate action help “protect spaces” where innovation, jobs and profits can emerge. Actors are moved by the incentives offered by these spaces but must engage in a series of “translations” to seize opportunities and reduce risks. Success in construction industry decarbonization requires that government, think-tanks, and firms understand the complexity of innovation trajectories and the trade-offs they entail.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 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".