Post-disaster recovery through the evolution of the <i>lakou</i>, a traditional settlement pattern
Bibliographic record
Abstract
Purpose Historically, post-disaster reconstruction policies and practice ignore the embedded knowledge of the affected population; the process following the 2010 earthquake in Haiti followed this trend. This paper aims to examine the production of social space in self-settled post-disaster settlements in Leogane and Port-au-Prince, Haiti, the paper demonstrates the role that traditional settlement patterns played in the production of social capital. Design/methodology/approach A multi-sited case study approach was implemented to uncover the patterns of the lakou , which is a primary Haitian, traditional settlement pattern reflecting the familial social structure, present in self-settled post-disaster settlements. The study took place between February and June of 2012, two years after the 2010 earthquake across settlements in Leogane and Port-au-Prince. Semi-structured interviews were conducted with 40 inhabitants across the settlements to uncover meanings attached to the creation of space. Together with behavior mapping and participant observations, the interviews were analyzed to validate the reproduction of the lakou . Findings This paper demonstrates that endogenous inhabitants create the lakou in post-disaster settlements in Haiti. This case study validates the resilience of the lakou , the inclusive nature of the lakou system, and the important role it plays in the production of social capital within post-disaster communities. Originality/value This study demonstrates the importance of traditional settlement patterns in post-disaster community well-being and it demonstrates the need to incorporate traditional settlement patterns into post-disaster planning strategies. Furthermore, the study validates that traditional settlement patterns support the production of social capital within a community.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".