Reducing the vulnerability of the urban poor to climatic change : experiences from Colombo and Dhaka
Bibliographic record
Abstract
In many megacities of the developing world the combination of rapid population growth and demand for land results in people settling in areas prone to natural hazards. Dhaka, Bangladesh and Colombo, Sri Lanka are no exceptions. This thesis discusses the challenges faced as a result of seal level rise and increased flooding, and solutions realized in urban areas of low and middle-income nations. It attempts to find solutions to reduce the vulnerability of these communities in context to housing and infrastructure. These regions contain a third of the world’s population, and in addition to having significant social and economic difficulties they also face high risks from volatile climatic conditions. Overall much of the urban populations in the developing world have no adequate infrastructure and live in poor quality housing. The initial part of the thesis analyzes the vulnerability of slum dwellers in Dhaka and Colombo by highlighting the major factors behind their sensitivity to floods and their ability to adapt to post disaster conditions. The low-income communities in South Asia have achieved successful innovations in disaster- risk reduction. The two case studies of Halgaha Kubura in Colombo and Korail in Dhaka exemplify these successful innovations and, as such, represent the primary research of this thesis. These case studies demonstrate the importance of understanding hazards, socioeconomic, resource availability, vulnerability, social networks, and current construction of housing and infrastructure. Transect mapping is used as a tool to map these locations, through which significant lessons can be drawn. Knowledge of existing coping strategies for disaster risk reduction can help to strengthen planning strategies of adaptation to climate change elsewhere. Finally, though these slum dwellers have their own adaptation and coping strategies to overcome the crisis, they are not a viable long-term solution. The goal of the thesis is to build an Adaptation and Disaster Resilient Design Guideline by extracting significant lessons from the mapped locations while taking into account the current strategies developed by flood reduction programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".