Leadership in the Worst Possible Way: Worst Case Planning as a Strategy for Public Agencies
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 paper is to consider the utility of Clarke’s worst case planning by examining Y2K preparations at two U.S. 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 paper 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. 2
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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 | 0.024 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".