Convergence unlimited: overloaded call centres and the Indian Ocean tsunami
Bibliographic record
Abstract
In the wake of the 2004 Indian Ocean tsunami, hundreds of thousands of persons all over the world called their foreign ministries to report that they were concerned their loved ones were among the victims. There were so many calls that most Foreign Ministry call centres were overwhelmed in short, there was worldwide information convergence. Though all call centres had problems some fared better than others, sometimes because they had more experience or better planning, sometimes because they had a good back-up system or because they had a recording informing callers what information would be needed so callers were prepared when they did get through not because they did get through. In one case the problems were fewer because the incident occurred the day after Christmas day, which is a holiday in Christian countries but was a normal working day in Israel. Two countries Canada and the Netherlands used a computer-based system designed by a Canadian company, World Reach Software, intended for precisely this type of crisis. It functioned well. There is no way to prevent calls in the wake of such destructive events but a review of what happened in nine countries Israel, the Netherlands, the UK, Denmark, Norway, Sweden, Canada, Australia and New Zealand suggests that some lessons were learned and that planning could be improved.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".