Navigating food futures post-disaster: the intersectional politics of race, gender, disability and access
Bibliographic record
Abstract
A historically devastating series of climatic events in September 2017 transformed lives in Puerto Rico and its diaspora. The impacts of Hurricanes Irma and Maria ruptured the functionality of lifeways in unprecedented forms. The events also characterize the longest disaster response muddle in the history of the United States. This focused case study investigates the ways that individuals navigate their island food system in a post-disaster context. It explores the political, social and economic circumstances which inform various experiences of food insecurity, hunger, gendered vulnerabilities, disability and the role that layered structural inequity plays in producing unequal access to food. Based on news media, photographic analysis and in-depth interviews conducted with those who lived through both hurricanes, an evidence-informed intersectional analysis is produced. Scaffolding the research conceptually through an intersectionality and Afro-diasporic futures epistemology, this paper contributes a geographical and feminist analysis to the study of disasters in relation to food insecurity. Markedly, people living with disabilities and women participants reported increased challenges to disaster recovery and resilience. Parents were found to be the most food insecure. Research findings show that gender, race, disability and income play a pivotal role in shaping food access and long-term wellbeing in post-disaster contexts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.018 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".