Why Rebuild on Toxic, Sinking Ground?: The Challenges for Disaster Recovery in Southeast Louisiana
Bibliographic record
Abstract
As southern Louisiana is experiencing one of the highest rates of sea level rise in the world, it is not uncommon for residents to hear that it is “too late” to save their homes from the impacts of climate change. Particularly, in the wake of disaster events such as hurricanes and oil spills, heavily damaged areas are often left behind in the recovery process as few developers are willing to take the capital risk to rebuild a sinking neighborhood. Still, some of these residents refuse to be moved and their resilient spirit is widely celebrated. Cultural resilience alone, however, is not enough to resist the onslaught of climate disasters nor counter systemic disinvestment in their communities. Through combining historical and ethnographic insights from the Black residents in Cancer Alley, the Vietnamese refugee community in New Orleans East, and the Indigenous tribal members of the Grand Bayou Village, this article argues that marginalized landscapes and livelihoods have been structurally made to become untenable within the economic bounds of disaster recovery. Under these circumstances, Louisiana’s coastal communities continue to assert survivance within precarious environments, offering alternative narratives to blind optimism or defeatism for living in an age of climate crisis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".