Fairness under fire: Environmental justice, mental health, and natural disasters
Bibliographic record
Abstract
Natural disasters are increasing due to climate change, bringing with them substantial increases in disaster-associated mental illnesses, such as depression, PTSD, and anxiety. Previous evidence has shown that after a natural disaster, these mental heath outcomes are not distributed equally throughout the population, but tend to affect certain groups of people more than others. Yet, inequality does not necessarily constitute an inequity. Currently, there is no established way of determining the fairness of mental health outcomes post-disaster, which is a necessary component of determining whether policies or guidelines ought to change in order to remedy an injustice. In this project, I use an environmental justice framework to assess the justness of mental health outcomes after natural disasters, using the Fort McMurray fire of 2016, known as The Beast, as a case study. Environmental justice theories have not previously been used to determine justness of mental health outcomes after natural disasters, therefore I begin by determining whether this the correct type of theory to use for this endeavour by examining certain critical components of the theory against what would be required for its application in this particular context. I end this ethical analysis by suggesting particular elements for inclusion in an environmental justice theory, to accommodate its usage for mental health outcomes post-natural disaster. The Beast caused the largest mandatory evacuation and was the costliest disaster in Canadian history. It therefore serves as a highly relevant case study to examine the question of equity in mental health outcomes in a Canadian context. Using aggregated data from Alberta Health, academic articles, newspaper articles, and published reports, I attempt to determine what the mental health outcomes of the Beast were, and if they affected the members of the population equally. In my final chapter, I applied the findings from my ethical analysis to the case study. This iterative process highlighted gaps and strengths in the approach. I conclude this thesis by reflecting on the learnings from this application process and offer thoughts on how we can move forward.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.024 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".