Building urban resilience through sustainability-oriented small- and medium-sized enterprises
Bibliographic record
Abstract
The unfolding COVID-19 pandemic, and the unprecedented social and economic costs it has inflicted, provide an important opportunity to scrutinize the interplay between the resilience of small and medium-sized enterprises (SMEs) and the resilience of the communities they are embedded in. In this article, we articulate the specific ways that SMEs play a crucial, and underappreciated role in building resilience to human and natural hazards, and provide new opportunities to accelerate the adoption of sustainability practices through the configuration of 'enabling ecosystems' geared towards promoting sustainability in the private sector. We argue that capacity-building and experimentation are not only required within companies, but also throughout this emerging supportive ecosystem of policies, resources (i.e. finance, materials, skills), governance actors, and intermediaries to adequately focus investment, technical capabilities and innovation. Ultimately, we call for a new transdisciplinary action research agenda that centers on SMEs as pivotal actors and amplifiers of community resilience; while recognizing that these firms are themselves in need of support to secure their own capacity to respond to, and transform in light of, crises. This research program calls for recognizing and applying the lessons that the pandemic presents to the urgent need for accelerated climate action. This will be enabled by developing more targeted approaches to collaborative capacity-building activities in SMEs that feed into experimentation and allow for the accelerated adoption of deliberate and strategic resilient business practices and models.
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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 | 0.001 | 0.003 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".