Towards Franchising Mobilization Strategy in Large-Scale Energy Efficiency Retrofit Industry
Bibliographic record
Abstract
Implementing large-scale energy retrofit projects for reducing residential energy consumption requires large scale mobilization and training of contractors with the appropriate skill sets for carrying out the retrofits. General contractors, one of the key parties in these types of projects, play a crucial role in facing the challenge of large-scale, national level mobilization. Most retrofit projects are performed in a fragmented manner, that is, a small percentage of contractors undertake executing all the retrofit tasks, and other contractors, typically trade subcontractors, prefer to operate only in their specific fields of proficiency. In addition, most general contractors operate in very geographically specific markets, limiting their market share and access. We propose a transition step in the conduct of energy efficiency retrofits by adopting a franchising business model as a leveraging strategy for general contractors in the retrofit industry. This study is being carried out to investigate the conceptual and practical benefits of franchising a mobilization option for large-scale energy retrofit industry. Based on the nature of this industry, a practical franchising arrangement is also proposed for this sector.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".