Taking Action on Green Resilience: Climate Change Adaptation and Mitigation Synergies
Bibliographic record
Abstract
Climate change impacts are already causing environmental, social, health, and economic problems for Canadian communities, and these are projected to increase. There is widespread recognition that we must plan responses to these impacts (climate change adaptation), and that reducing greenhouse gas (GHG) emissions (climate change mitigation) is a crucial priority if we are to minimize them. Communities can maximize the effectiveness of actions and increase funding opportunities by advancing these approaches through integrated “Green Resilience” (GR) strategies.\nThis report draws together content and conclusions from a workshop entitled “Taking Action on Green Resilience” hosted by ACT, SFU and the consulting firm Green Resilience Strategies (GRS) at the 2017 ICLEI Canada Livable Cities Forum in Victoria, BC. The workshop brought together 40 public and private sector climate change practitioners from across Canada with expertise in urban planning, municipal policy, energy systems, buildings, engineering and communication. This report provides examples of GR measures, summarizes key benefits, provides insights on how to identify, fund and implement GR opportunities, and recommends new or updated research, analysis, technical assistance, incentives and regulations identified by participants as necessary to advancing GR practices.
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 | 0.000 | 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 | 0.000 |
| 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 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".