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Record W4233126966 · doi:10.3138/jcs.40.1.37

Hurricane Hazel: Disaster Relief, Politics, and Society in Canada, 1954-55

2006· article· en· W4233126966 on OpenAlexvenueaboutno aff
Danielle Robinson, Ken Cruikshank

Bibliographic record

VenueJournal of Canadian Studies · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsStormNatural disasterGovernment (linguistics)Local governmentPolitical scienceEmergency managementPublic administrationGeographyLaw

Abstract

fetched live from OpenAlex

On Friday 16 October 1954, Hurricane Hazel generated flash floods in the watersheds surrounding Toronto. Flooding destroyed bridges, engulfed trailer parks and residential areas, and swept automobiles, trailers, cottages and homes into the strong current. In this essay, the authors explore the ways that the federal and provincial governments interacted with voluntary organizations and local governments to deal with the immediate crisis produced by Hazel’s floods, and how they negotiated the lengthy process of restoration. The responses of those governments tell us much about the social and environmental assumptions as well as the political capacity of Canadian society in the mid-1950s. The federal and provincial governments immediately promised action, but then reluctantly became involved in reconstruction, leaving as much responsibility as possible to voluntary organizations and local governments. A tropical storm travelling through the province of Ontario was a relatively rare event, yet ultimately government officials did not respond to the Hazel disaster as a random, chance event. Instead, the conservation movement and local authorities pressured governments to see the hurricane flooding not as a natural disaster, but as a tragedy, which human decisions had helped precipitate, and which, in the future, human decisions might alleviate.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.261
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2006
Admission routes2
Has abstractyes

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