The problem of doing more: success and paradoxes in scaling up informal initiatives for disaster risk reduction and climate action
Bibliographic record
Abstract
Development studies highlight the importance of scaling good practices and their replicability and transferability to face global warming. But what happens when practices originate in informal urban contexts? Should they be replicated, amplified and formalized? We explore the opportunities and contradictions that emerge in scaling disaster risk reduction in informal settings. For four years, we documented 24 local initiatives and the work of leaders in Latin America. Results show that impact depends on intermediaries, trust, dialogue and a delicate balance between conflicting objectives and different levels of involvement by externals. To succeed, initiatives must address “the problem of doing more.”
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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 | 0.073 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.014 | 0.068 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".