Small and Medium Enterprises in the Americas, Effect of Disaster Experience on Readiness Capabilities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Disaster risk reduction (DRR) is key in strengthening resilience and achievement of sustainable development. The private sector is co-responsible for DRR: it is a generator of risks, and a subject exposed to risks. There are competing narratives in the literature regarding the relationship between business’ disaster experience and DRR. The current study defined and characterized businesses in the Americas, with a particular interest in small and medium enterprises, and examined whether disaster experience predicts DRR, considering business size. Secondary data analyses were conducted using data from a previous study on private sector participation in DRR conducted in six Western Hemisphere cities (N=1162): Bogotá, Colombia; Kingston, Jamaica; Miami, USA; San José, Costa Rica; Santiago, Chile; and Vancouver, Canada. Results confirmed that business size matters – small businesses had lower levels of DRR efforts compared to medium and large businesses. Disaster experience (i.e., supply chain disruption, loss of telecommunications, power outage, and damaged facilities) predicted DRR. The findings underscore the importance of fostering, advising, and financing small and medium enterprises to proactively develop capabilities in the line of risk and emergency management, and early resumption of operations, post-disasters. Governing agencies and civil society organizations have the ability to provide this support.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it