Novel Multisector Networks and Entrepreneurship: The Role of Small Businesses in the Multilevel Governance of Climate Change
Bibliographic record
Abstract
While some jurisdictions are demonstrating leadership on climate change, it is clear that sufficient mitigation of climate change is not occurring. This highlights the importance of innovative approaches that bolster politically fraught international treaties and voluntary networks with strategies that exploit the strengths of a variety of traditional and nontraditional actors. With this paper we examine just such an innovation in the form of a multisector and multilevel network linking together the regional authority Metro Vancouver in the Canadian province of British Columbia, several municipal governments, a social enterprise, and a large number of small and medium-sized enterprises to act on climate change. This case demonstrates that while complementarity of actions across levels and sectors is not always achieved, it is nonetheless likely to contribute significantly to greenhouse gas emission reductions in the urban context. Interview and survey data also highlight that each sector and level of governance can provide what it is good at or capable of in order to enable others to contribute their share. Whether this is done on an ad hoc basis or in the form of partnerships, networks or agreements may vary from case to case, and further research is needed to understand what forms of multilevel and multisector partnerships, networks, and agreements are most conducive to achieving desired outcomes.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".