Unconventional Crises, Unconventional Responses: Reforming Leadership in the Age of Catastrophic Crises and Hypercomplexity
Bibliographic record
Abstract
Since the 1990s, North America and Europe have confronted a series of unconventional, catastrophic, or 'hypercomplex' crises. On the one hand, the U.S. and Canada have faced 9/11, the anthrax crisis, the SARS outbreak, and Hurricane Katrina. Meanwhile, Europe has been hit by the 'Mad Cow' disease, the 2003 heatwave, and 2007 forest fires in Greece. In addition, both sides were involved in the response to the 2004 Indian Ocean tsunami.Events such as these have destabilized, or even overwhelmed, traditional mechanisms for planning, response, and recovery. They have called upon leaders and analysts to develop new frameworks of interpretation, strategic guidelines, and roadmaps for action. All too often, in the absence of such insights, response efforts have tragically fallen short, and have been followed by a litany of after-event reports that typically have failed to get to the root of the problem. To tackle this issue, the Center for Transatlantic Relations in 2006 launched the project 'Unconventional Crises, Unconventional Responses' under the leadership of Dr. Erwan Lagadec. The project sets up a cross-sector, international platform of leaders and experts. Based on the results of a seminar convened in Washington, D.C. in March 2007, this book develops innovative diagnoses of current deficiencies in crisis-management concepts, and lays out proposals for reform.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".