Framing responses to post‐earthquake Haiti
Bibliographic record
Abstract
Purpose The aim of this paper is to add a new dimension to urban resilience by exploring how representations of disasters, reconstruction and human settlements are made, and how, by shaping plans and programs, they ultimately influence resilience. Design/methodology/approach The paper draws on James Scott's notion of “legibility” to ask how different representations simplify complex realities and how they are transformed into plans and programs. The paper first outlines the various broad analytic lens used to examine legibility to portray post‐disaster reconstruction, drawing on international literature and policies. The paper then focuses on post‐earthquake Haiti and analyzes eight reconstruction plans and reviews design proposals submitted for the Building Back Better Communities program to explore how different stakeholders portrayed the disaster, identified the reconstruction challenges and proposed to address human settlements. Findings Representations of the disaster, the reconstruction challenge and the housing problem were quite varied. While the plans assumed a very broad view of the reconstruction challenge (one that goes beyond the representations found in the literature), the BBBC program adopted a very narrow view of it (one that the literature condemns for failing to achieve sustainable resilience). Research limitations/implications The empirical research is exploratory, suggesting an approach that throws a new light on the analysis of plans and programs for improved resilience. Practical implications The study suggests that the representations that decision makers, institutions and organizations make of the world ultimately establish the framework in which resilience is constructed. Originality/value The lens of legibility confirms that the expression of different representations makes the world legible in different ways and therefore transforms the way in which resilience can be improved.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".