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 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 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".