Barriers and enablers of local adaptive measures: a case study of Bengaluru’s informal settlement dwellers
Bibliographic record
Abstract
Cities, with their increasing populations, are host to a range of issues including non-climatic factors due to the prevailing development paradigm, discriminatory urbanisation patterns, and weak governance structures. Climate change poses an additional challenge and exacerbates existing vulnerabilities affecting cities and its people, especially the urban poor. This paper highlights the barriers and enablers to climate change-related adaptation experienced in some of Bengaluru’s informal settlements. The barriers described in the paper include economic, social, governance and information related issues that impede local actions and increase vulnerabilities. Enabling factors such as improving social and human capital, gaining formal recognition and most importantly support from agencies (e.g. local government, civil societies, and community leaders), help overcome some of the barriers or challenges. Hence, local level adaptation measures mainstreamed with local developmental agendas help address some of the structural causes of vulnerability. Contextual policies and interventions can facilitate successful local level adaptation measures.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".