Bibliographic record
Abstract
Organization theorist Lee Clarke (2005) argues when policy makers plan for disasters, they too often think in terms of past experiences and “probabilities.” Rather, policy makers, when planning to protect the infrastructure, should open their minds to worst-case scenarios; catastrophes that are possible but highly unlikely. Underpinned by a precautionary principle, such an approach to the infrastructure would be more likely to produce “out of the box” thinking and in so doing, reduce the impact of disasters that occur more frequently than people think. The purpose of this chapter is to consider the utility of Clarke’s worst-case planning by examining Y2K preparations at two US government agencies, the Bureau of Labor Statistics (BLS) and the Federal Aviation Administration (FAA). The data concerning Y2K come mostly from official US government sources, interviews, and media analysis. The chapter concludes that the thoroughness of worst-case planning can bring much needed light to the subtlety of critical complex and interdependent systems. But such an approach can also be narrow in its own way, revealing some of the limitations of such a precautionary approach. It potentially rejects reasonable efforts to moderate risk management responses and ignores the opportunity costs of such exhaustive planning.Request access from your librarian to read this chapter's full text.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".