Watts at Stake?: Protecting North America’s energy infrastructure from cascading failure and terrorist threats
Bibliographic record
Abstract
Due to rising consumption, electrical infrastructure has grown in size and complexity. This has allowed for an increased vulnerability of the infrastructure. Under the caveats of high-reliability organisations (HRO) theory and normal accidents theory (NAT), this paper examines two predominant threats to the North American energy sector: cascading failures and terrorism. A key consideration underlying the analysis is that NAT and HRO are not mutually exclusive; i t is within both theories to suggest that redundancy and organisational learning are essential for the operation of critical energy infrastructure. This paper argues that while energy infrastructure has several characteristics of an NAT organisation, the high-consequence nature of infrastructure operations lends to a predisposition towards HRO strategies for risk identification and management. Energy infrastructure must be highly reliable, because society expects it to be so – the capacity for meeting periods of high demand must not be disabled by accidents or attacks. Due to rising consumption, electrical infrastructure has grown in size and complexity. This has allowed for an increased vulnerability of the infrastructure. Under the caveats of high-reliability organisations (HRO) theory and normal accidents theory (NAT), this paper examines two predominant threats to the North American energy sector: cascading failures and terrorism. A key consideration underlying the analysis is that NAT and HRO are not mutually exclusive; i t is within both theories to suggest that redundancy and organisational learning are essential for the operation of critical energy infrastructure. This paper argues that while energy infrastructure has several characteristics of an NAT organisation, the high-consequence nature of infrastructure operations lends to a predisposition towards HRO strategies for risk identification and management. Energy infrastructure must be highly reliable, because society expects it to be so – the capacity for meeting periods of high demand must not be disabled by accidents or attacks.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".