Flood hazards, environmental rewards, and the social reproduction of risk
Bibliographic record
Abstract
People pursue the environmental rewards that come with proximity to water despite associated flood risks. Governments are complicit in this exposure to flood hazards and prevailing institutional arrangements support it, begging the question of why all these actors keep making decisions that serve to reproduce risk in a society that should know better. This paper seeks to address the question by broadening the ontology of social reproduction. It draws on three disparate “imaginaries” of risk—the production of risk, risk and the social contract, and risk at the intersection of capital and rule—to make conceptual links between the production of nature, capitalist production, and social reproduction. This approach is then applied to a study of flood risk on Toronto Island, Canada. Investigating how residents there interact with environmental amenities and acute flood hazards, and how those institutionally-supported interactions are shaped by the Indigenous, environmental, and recent histories of the island, calls for a broadened social reproduction theory to hold together the general and the particular in the contingent geographies of risk. It reveals that flood risk is socially reproduced by processes working in concert to facilitate powerful groups of people in their pursuit of environmental rewards while marginalizing others, exclude some groups of people from the social contract, and situate risk as an organizing point around which lopsided bets are made within the capitalist political economy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".