Bibliographic record
Abstract
In the summer of 2013, just as a small town in Quebec was decimated due to a train derailment, heavy rainfall prompted thirty Alberta communities to declare a state of emergency. Whereas a SWAT team surrounded train conductor Thomas Harding and brought him to court where he was charged with the deaths of forty-seven in Quebec, Calgary mayor Naheed Nenshi emerged from the Alberta crisis as a folk hero. As the Lac-Mégantic train derailment and the flood in Alberta demonstrate, political, economic, legal, and cultural climates influence the way disasters are received and managed. In Too Critical to Fail, Kevin Quigley, Ben Bisset, and Bryan Mills identify the social context that shapes the Canadian government’s ability to prepare for and respond to emergencies. Using original research on natural disasters, pandemics, industrial failures, cyber-attacks, and terrorist threats, the authors evaluate the risk regulation regimes that monitor, interpret, and respond to failures in Canada’s critical infrastructure to limit their possibilities and consequences. More broadly, this book identifies key vulnerabilities and regulatory challenges for both the government and the private sector in mitigating threats to safety and security. Too Critical to Fail applies an investigative lens to the multiple and competing risks that the government balances to secure assets that enable modern civilization. Raising questions about Canadians’ ability to protect critical infrastructure and respond to threats, this book challenges the biases that determine who is held to account when the system fails.
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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.023 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".