Construal Of Voluntary Environmental Program Participation In The Canadian Residential Construction Industry
Bibliographic record
Abstract
This is a study of sensemaking of how Canadian new home builders construe their decision to participate in a voluntary environmental program (Built Green Canada). The Repertory Grid Technique was the data collection method. Findings from 32 interviews revealed a number of themes that decision makers used to make sense of their decision to participate in the program. The most prevalent views related to seeing the decision as a function of being a leader or innovator in the industry and using the program as a marketing and sales tool. Furthermore, themes that were seen as important related to legitimacy/authenticity/integrity and environmental impact. An assessment of which drivers/pressures were important to decision makers in making the decision to join the program was also undertaken. Important drivers/pressures included handling competition, appealing to customers, acquiring technical knowledge, obtaining publicity, building corporate culture/identity, and obtaining third party certifications. The findings are significant as it assists in refining the emergent field of environmental decision making and planning. The results are also useful for industry, voluntary environmental program organizations, and government policy makers to provide them with a better understan
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".