Bibliographic record
Abstract
Communities in Canada have influence over nearly 50% of Canadian greenhouse gas emissions and stand on the frontlines of climate change impacts. In order to meet energy objectives, continued coordinated action at the municipal level is essential. However, many municipal governments are constrained with regard to both human and financial capacity. These constraints reduce the ability of communities to seek out the necessary information on best practices and available funding to drive needed changes. The Municipal Energy Learning Group in Nova Scotia serves as a resource for knowledge mobilization among municipal staff and for these staff members to gather relevant information, learn about successful plans, visit projects in action, and network with their colleagues. For the past three years, with support from the Nova Scotia Department of Energy and Mines, QUEST (Quality Urban Energy Systems of Tomorrow) has experimented with various methods of bringing municipal staff from different local governments together, including webinars, facilitated peer-to-peer meetings, workshops, and study tours. Facilitating this group has allowed for an identification of trends in the barriers and opportunities faced by municipalities with regard to climate change, but also in the effectiveness of this model in delivering benefits to the members. The use of inspiration and celebration of success has been an important factor in affecting change. Also, the involvement of representatives from multiple departments has shown that everyone has valuable experience to share and increased engagement and knowledge transfer. The Municipal Energy Learning Group (MELG) in Nova Scotia serves as a resource for knowledge mobilization among municipal staff and for these staff members to gather relevant information, learn about successful plans, visit projects in action and network with their colleagues. For the past three years, with support from the Nova Scotia Department of Energy and Mines, QUEST (Quality Urban Energy Systems of Tomorrow) has experimented with various methods of bringing municipal staff from different local governments together, including webinars, facilitated peer-to-peer meetings, workshops and study tours. Facilitating this group has allowed for an identification of trends in the barriers and opportunities faced by municipalities with regard to climate change, but also in the effectiveness of this model in delivering benefit to the members. The use of inspiration and celebration of success has been an important success factor in affecting change. Also, the involvement of representatives from multiple departments has shown that everyone has valuable experience to share, and increased engagement and knowledge transfer.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".