Resilience in the Context of Pandemics and Disasters: A Framework for Public Governance, Ecosystem Innovation, Co-creation, and Co-production
Bibliographic record
Abstract
This article, through a literature review, explores evidence that public governance positively influences and stimulates the innovation ecosystem, the co-creation, and co-production of resilience in communities that are victims of disasters, shocks, and even pandemic disasters, such as COVID-19. Disaster scenarios of all kinds cause millions of damage to society. The negative impacts of these contexts disasters, shocks, catastrophes, even if pandemic, such as COVID-19, can be greater or lesser, depending on the dysfunctions of governance and the negative barriers to the creation of public policies consistent with the capacity for resilience. Thus, the possible solution to this scenario may be based on public governance, if the pubic governance is instituted in an organized, integrated, and articulated manner. For this purpose, a theoretical conceptual framework of value creation is proposed through public governance that values and stimulates the innovation ecosystem, the co-creation, co-production of value, and a static, hierarchical, and linear structural vision. There is theoretical evidence that public governance can promote resilience in communities that are victims of disasters, shocks, pandemics or disturbances, through an ecosystem of innovation, co-creation and co-production of value. In this regard, at the end of this article, a theoretical-conceptual framework for creating public value is proposed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".