Interrogating the Ruling Relations of Thailand’s Post-tsunami Reconstruction: Empirically Tracking Social Relations in the Absence of Conventional Texts
Bibliographic record
Abstract
This paper discusses methodological strengths and challenges in doing institutional ethnographic (IE) research in communities devastated by the 2004 Indian Ocean tsunami in Southern Thailand. IE is a mode of inquiry used to describe institutional mechanisms of reconstruction, aid, and recovery and to show how recovery efforts affected real people and communities over time. The chaotic nature of a disaster zone, combined with the more common difficulties of conducting research in a developing region relying on a translator, posed various challenges in the conduct of this IE study. Textual data, one of the important tools used in IE research, were scarce and what texts emerged were unusual. Our study reveals a disordered and uneven aid distribution. We show how private interests and pressure for economic redevelopment coordinated government practices which could be portrayed as "corrupt." Our paper highlights the strengths of the IE method in assessing reconstruction, aid, and recovery in a disaster zone by focusing on the everyday lives of people as they moved beyond the immediate turmoil. We discuss the methodological techniques used to uncover empirical data to support analytical work when actual texts were not available. Further, we describe how IE is an effective approach for examining peoples' recall of past events, where experiences described can provide insights into the current social organization and ruling relations. These insights lead to our understanding of changes and developments that occurred in the landscape and in the community after recovery. We discuss how the reconstructed environment, including new buildings and signage, coordinated and changed people’s day-to-day activities and their ways of making a livelihood.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".