Material Resistance and Legal Persistence:A Case Study of Hurricane Katrina’s Storm Levy Infrastructure and the Katrina Canal Breaches Consolidated Litigation
Bibliographic record
Abstract
Because Hurricane Katrina had such a dramatic effect on the material infrastructure of New Orleans, it can be used as a case study to better understand exactly how and to what extent infrastructure effects social institutions, and also to what extent infrastructure exacerbates the fragile relationships between marginalized communities and the institutions that govern them. Through the analysis of the Katrina Canal Breaches Consolidated Litigation, this research employs the ethnographic and political analysis of disaster infrastructures and their related sociolegal infrastructures to reveal Hurricane Katrina as an essential example of infrastructure failure. In doing so, this paper will reveal how the breakdown of both New Orleans' storm levies and its socio-legal protections (meant to safeguard vulnerable victims of natural disaster) reveal the power of infrastructure to shape social relations, form metonyms of marginality, and produce the conditions for social assembly. To that end, this research draws upon the following concepts: the politics and poetics of infrastructure (Larkin, 2013), the promise of infrastructure (Appel et al., 2018), the ethnography of infrastructure (Star, 1999), and infrastructural resistance (Audette Longo, 2017; Easterling, 2014). By analyzing disaster infrastructures, both material and sociolegal, my research ultimately aims to reveal infrastructures as media of politicsthe material sites in or at which we can catch glimpses of what politics is and how it works.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.015 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".