Bibliographic record
Abstract
On the morning of 11 September 2001, two hijacked aircraft smashed into the twin towers of the World Trade Center in New York City.First one, then the other tower collapsed.Before that happened, most of the people evacuated the two buildings, not in panic but, for the most part, slowly and efficiently.Even as they were doing so, emergency personnel, some assigned and many not assigned, arrived at the scene.Though an earlier somewhat similar incident showed the danger and futility of entering a flaming building, New York's fire department sent firefighters up into the building.When communications broke down, many of those men died in the subsequent collapses.Hundreds of other people also responded, including medical personnel from city hospitals, and volunteers from as far away as Newfoundland.Because the incident made normal transportation impossible, scores of ferries and other boats began to move people from Lower Manhattan-though this was not part of any plan.The result was the largest evacuation by water since the British Army fled Dunkirk in 1940.Much of this response was predictable.Today, it is well known that panic is rare in such incidents, and that people generally perform quite well.It is also well known that organizations do less well and that over-response by emergency personnel is normal.And it is known that, in times of crisis, emergent organizations take on tasks that were not planned for, just as the New York ferries did on 9/11.The reason this was predictable is that a number of scholars, many of them associated with the Disaster Research Center, at the University of Delaware, have systematically studied events like 9/11.Those studies have shown there are predictable patterns of behaviour in such incidents.Most of this research has been done in the United States since World War II.But there was one major study published many years earlier.It was done by a Canadian Anglican priest, Samuel Henry Prince, and it was a study of Canada's worst catastrophe, the 1917 Halifax explosion.The story of this event has been told many times, not just in Prince's doctoral dissertation for Columbia University, but in novels, non-fiction accounts, and television
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.637 | 0.472 |
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".