A Case Study Of 100 Resilient Cities: Does The 100 Resilient Cities Model Provide For A Robust Decision-Making Framework?
Bibliographic record
Abstract
Background: Rapid urbanization continues to occur on a global scale with the majority of the world’s population residing in cities of various sizes and scales. Cities and their residents are becoming increasingly vulnerable to climate change and its impacts. Cities will continue to face social, political and economic impacts which particularly affect the most vulnerable populations. Municipal governments have focused upon resistance and control when dealing with complex problems such as natural disasters and their impacts. This research focuses on the 100 Resilient cities Model to assess its robustness as a decision-making framework in relation to resilience and adaptive governance. Methods: This researches relies upon 100 Resilient Cities as a case study. This project utilizes qualitative analysis of the 100 Resilient Cities model and critical assess its robustness through review of ecological and social-ecological resilience literature. Conclusions: This paper concludes that the 100 Resilient Cities model is well-grounded in ecological and social-ecological systems literature. There is potential for the 100 Resilient Cities model to provide urban planners and policymakers with an effective decision-making tool in order to solve complex problems which exist within municipal governance structures.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".