Development and demonstration of an energy feedback research platform in a field study with real-time social comparisons
Bibliographic record
Abstract
Providing residential tenants with feedback on their energy use can be an effective intervention, promoting savings ranging from 4-12%. However, advancements in feedback design have been hindered by methodological limitations, the lack of specification of visual feedback designs, and a poor understanding of the behaviour changes that are induced by feedback. This thesis presents the design and demonstration of an Internet-of-Things-based feedback research platform, which was intended to help address these issues, and which will be made freely available for re-use and reconfiguration. Configured for a rental apartment building in Toronto, Canada, the platform was a central component of a conservation program and field study examining the efficacy of real-time social comparisons. Results showed a statistically significant effect of the conservation program with a relative year-over-year, weather-normalized savings of approximately 11%. An encouraging, but non-significant, finding of a 3.5% relative improvement with real-time social comparisons warrants future large scale studies.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".