Partnering for Climate Resilience: Exploring the Maturity of Private-Sector Efforts in the Great Lakes Region
Bibliographic record
Abstract
Communities and businesses continue to experience the effects of climate change as global temperatures rise and extreme weather events become more frequent. In the United States (US), the public sector has traditionally been responsible for mitigating these risks; however, engaging the private sector is crucial, given industrial impacts on and vulnerability to climate change. Private-sector mitigation and adaptation efforts are critical in the Great Lakes Region due to aging infrastructure as well as its economic, environmental, and political importance in the US and Canada. This study explores private-sector resilience efforts in three Great Lakes cities to identify opportunities and trends that could inform climate resilience strategies in the region. Climate-related commitments and actions of nine major firms in Toronto, Chicago, and Cleveland are evaluated in relation to seven climate resilience criteria on a five-level maturity scale from January to May 2022. The results indicate a moderate level of maturity, with efforts mainly at facility and community levels of engagement. Overall, this study suggests that major firms participate in climate resilience efforts, but to a limited extent, and may have varying priorities that affect the initiatives they pursue. This study could contribute to advancing climate resilience efforts in the public and private sectors from regional to global levels.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".