Bibliographic record
Abstract
Legendary deluges such as those said to have over-swept the Tamil lands or the flood waters that appear in popular religious and folk tales have long been a part of Tamil folk experience, and they serve as the backdrop against which contemporary flood is experienced. In this light, this dissertation explores the development and of disaster management policies in the Union Territory of Pondicherry from their origins in colonial-era policies to the significant re-orientation that followed the 2004 Asian tsunami. Conclusions are based on 14 months of ethnographic research in coastal fisher communities and government relief agencies in the Union Territory. Historical data collected from archives and interviews with territorial officials and NGO workers complement insights gleaned from extensive participant-observation and field collection among deep-sea fisher populations in the former French territories of the Coromandel Coast. Part one defines a Tamil “flood imaginary” by exploring myth-historic instances of flood in the Tamil-speaking region of India. The study then examines flood in the French colonies of India during the 18th and 19th centuries. Together these provide the background for better understanding the policies and beliefs about flood in place prior to the 2004 Asian tsunami and the effects these had on preparedness and resilience at both community and administrative levels. Part two focuses on the ways in which these affected how the territorial government and at-risk communities responded to the 2004 tsunami. Tensions that arose between government and community post-tsunami are examined through the interrogation of documents of agencies that undertook rehabilitation. “Official” narratives of relief and reconstruction are balanced against the perspectives of recipients of government and voluntary aid and the local panchayat leaders who are agents of first resort for lodging requests and grievances. Through a comparison of relief efforts taken within a single state, this research higlights the efficacy of an approach to disaster relief and mitigation planning that appropriately integrates outside expertise with community metis and demonstrates the value of policy informed by ethnography.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".