Bibliographic record
Abstract
Energy supply is a major contributor to global greenhouse gas emissions. Smart grids, technologies which integrate information and communication components into the electricity grid, have emerged as a cost-effective means of mitigating energy supply emissions, optimizing the integration of intermittent renewables and creating new opportunities for demand side management. \n \nSmart grid projects have been launched in many Canadian provinces, however few of these projects have succeeded beyond the pilot stage. In order to learn and benefit from these smart grid experiments, the literature suggests documenting these projects in detailed case studies. \n \nThis paper presents a case study of Heat for Less, a smart grid project in Summerside, Prince Edward Island that links the City’s excess wind capacity to smart appliances, sold and installed in the homes of residents, which store electricity in the form of heat. Drawing on desktop research and semi-structured interviews, this paper details how and why Heat for Less moved beyond the pilot stage and into wide-scale deployment. Additionally, this paper analyzes these findings by applying three frameworks from the sustainability transitions literature: strategic niche management, the multilevel perspective, and the transition pathways. \nThis research found that context is critical to understanding how this project moved along the innovation chain. In addition to the technological aspects of Heat for Less, the politics and social dynamics at play in the City of Summerside significantly contributed to the success of this project. \nFuture researchers might consider expanding upon this study by surveying the early-adopting homeowners who purchased smart appliances. Further, researchers might also consider transferring the methodology used in this paper to a similar smart grid project for comparative purposes.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.022 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".