Towards Franchising Mobilization Strategy in Large-Scale Energy Efficiency Retrofit Industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it