Weathering Storms and Flooded Waters: Anthropological Perspectives of Policy and Risk in Toronto, Ontario
Bibliographic record
Abstract
This dissertation examines how the interactive processes of policy construct social relationships, influence expert and lay perceptions of risk, and contribute to practices of risk management and decision making. In the context of weather and flooding in Toronto, Ontario, I first show how meteorologists at Environment and Climate Change Canada enact their roles as experts and attain operational success while adhering to policy constraints during ‘non-severe’ weather perceived as risky. Then, through an analysis of face and drawing on the concept of Goffman’s interaction ritual and Collins’s interaction ritual chain, I illustrate the role that ambiguous river flood situations play in shaping risk and policy interactions and decision making for flood forecasters at Toronto and Region Conservation Authority as well as various key recipients of their information. Finally, at the level of discourse, I compare policied river flood risk and unpolicied non-river flood risk in Toronto and uncover the entanglement of policy with organized government irresponsibility and heterogeneous public social realties. The three analyses encapsulate different, yet related policy and risk scenarios: when official policy exists, but the weather doesn’t meet risk threshold criteria; when official policy exists and the circumstances meet risk threshold criteria but there is uncertainty related to the atmospheric conditions which complicates the policy negotiation process; and finally, when no official policy exists and people, who are neither experts in meteorology nor hydrology, are left to identify and manage risk on their own. Through a combination of participant observation, semi-structured interviewing, and survey administration, the findings contribute to anthropology of policy and risk literature by illuminating the influential role of interaction in shaping these concepts and their related processes, and the effect interaction has in policy-work for propagating risk in unintended ways. The interconnections uncovered here have important implications for weather and flood policy-makers as well as policy implementers in Toronto as they look toward enhancing policy and risk management initiatives for the protection of the publics they serve.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".