Helping the Other Victims of September 11: Gander Uses Multiple EOCs to Deal With 38 Diverted Flights
Bibliographic record
Abstract
On September 11, 2001, after seeing three hijacked jets turned into missiles and a fourth crash in Pennsylvania, the United States ordered all U.S.-registered aircraft to land at the nearest airport and closed its airspace. When the decision was made, hundreds of commercial flights were over the Pacific or Atlantic en route to North America. Some had sufficient fuel to turn back. Most needed a North American airport to take them and that airport had to be in Canada. The Canadian government, its air traffic control system and Canadian airports were presented with a fait accompli. They had to accept hundreds of aircraft knowing-given what happened-that one or more of them might be carrying terrorists or be under terrorist control. Worried about the possibility that some of those jets might attack major Canadian cities, the federal government ordered that these jets land at smaller communities along Canada's East Coast. Two Canadian cities-Halifax and Vancouver-received the most diverted flights on September 11. But when Gander's population-10,347-is considered its intake was proportionally far greater. Gander took in 38 flights and 6,600 passengers, a 63 per cent increase in its population, compared to a two per cent increase in Halifax, less than a third of a one per cent increase for Vancouver. This article is about how Gander handled that situation. As will be shown, the community activated a number of emergency operations centers (EOCs)-and each ended up managing one aspect of the response. Though the airport was the key, the result was a coordinated system that ran smoothly without any single agency taking charge. This article describes how that system came about and why it worked; and how Gander avoided problems that often occur with multiple EOCs and emergent groups.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".