Assessing the societal impacts of green demonstration homes: a Canadian case study
Bibliographic record
Abstract
This article investigates the overall societal impacts of the REEP House for Sustainable Living (REEP House) in Kitchener, Canada. Available information on green demonstration homes (GDHs) is reviewed to identify their goals, past assessment practices and their impacts on different measures ranging from energy consumption to behavioural changes. From this, the need for a multicriteria framework for evaluating GDHs is demonstrated. Drawing upon the GDH experience, the broader impact assessment literature, knowledge gained from community-focused recreational events and information from open eco-homes, such a framework is developed. This five category GDH multicriteria framework is then applied to the case of the REEP House. Using both technical data and social data, the results provide unique insights into GDH societal impacts across a variety of areas. The REEP House’s retrofits had significant impacts: reductions of electricity consumption by 41 %, of water consumption by 94 % and of gas consumption by 78 %. Its programming activities also showed noteworthy effects: regarding information distribution, 76 % of visitors felt they had received enough material to improve their own home; and with respect to the overall impact, more than 50 % stated that they were planning to return to the REEP House. These results are compared with other GDHs’ experiences. In conclusion, lessons are drawn for all GDHs that wish to improve both their assessment procedures and their societal impacts. The limitations of this study are also identified.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".