How to survive a crisis
Bibliographic record
Abstract
How to survive a crisis is a remarkable book: beautifully written and thoughtfully designed, it mixes conceptual clarity and policy prescription with illuminating case-studies in a manner that is both unusual and fascinating. The contributors to this forum all agree that it is a book of immense importance; one that occupies an intriguing space at the crossroads of intelligence, security, politics, psychology and business, together with arresting personal experiences of some of the world's most frightening crises. The book exemplifies the best that policy studies have to offer. Patiently, and often narrating in the first person, Omand demonstrates how recent events are a vital sandpit from which we can—and must—learn. Sir David Omand is perfectly positioned to distil wisdom from these episodes. Omand joined the UK's Government Communications Headquarters (GCHQ) in 1969 and, after some time at the Ministry of Defence, he was instrumental in successfully reshaping GCHQ as its director in the mid-1990s. He then travelled—via the Home Office—to become the first Permanent Secretary for Intelligence and Security at the Cabinet Office in 2002, not long after the 9/11 attacks. Omand technically ‘retired’ in 2005, but in fact has since carried out numerous special roles from mysterious basement rooms under Downing Street. I remember asking a notably tight-lipped civil servant how the latest David Omand project was going? He simply grinned and replied, ‘Batman returns’.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.029 | 0.011 |
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".