A convergence of crises: COVID-19, climate change and bunkerization
Bibliographic record
Abstract
Bunkerization, a term often associated with military fortifications on 20th-century battlefields or the fallout shelters of the Cold War, can now refer to the building, buying and selling of artificial environments designed to provide protective and defensive responses to the ecological, military, and political threats of the Anthropocene. As places of elite retreat, however, these are not spartan spaces. This article documents how—for some—forms of bunkerization have emerged as privileged reactions or responses to contemporary environmental crises, such as climate change, by considering the case of last-chance tourism and luxury cruising. In 2020, both climate change and COVID-19 became intertwined as global crises emerging from humans’ troubling relationships with nature. To examine bunkerization as an individualistic reaction to these converging crises, we first outline the challenges presented by COVID-19 and its connections with human exploitation of animals and the environment. We then turn to the particular uses of the environment—in this case, the oceans—as locations of leisure and retreat, and offer an analysis of the image, operations and impact of the luxury cruise industry. In light of our current path of crisis accumulation, we conclude with an urgent call to adopt a more holistic view of planetary public health—one that includes not only humans but also other species and the natural environment.
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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 | 0.001 | 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) | 0.004 | 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 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".