Bibliographic record
Abstract
Does disaster strike only at the most inopportune times? Or do ordinary events become disasters largely as a result of their timing? Take the Great Flood of 2005, for example. If the hot water heater at Live Bait Theatre in Sackville, New Brunswick, had burst at a time when someone was around to notice, there might have been no crisis. Some one would just have turned off the water supply and called the plumber. Instead , the plumbing let go late on a grey March afternoon, just after everyone had left the theatre for an early supper, planning to be back in a few hours for the opening of Little Shop of Horrors, being presented that night by Black Tie Productions, a Mount Allison University student company. By the time the receptionist in the doctor’s office next door noticed a small stream winding across her waiting-room floor, water had been spewing from the broken tank for more than an hour, soaking everything within reach: carpets, walls, stairs and box upon box of props and costumes, scripts, posters and memorabilia.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".