Bibliographic record
Abstract
In the spring of 1941, during the latter days of the London Blitz, St. Andrew’s Convent School in Leatherhead, seventeen miles southwest of Charing Cross, was badly damaged by a parachute mine. The nuns evacuated, turning the ruined building over to the county. The mine exploded in the bend of the elbow-shaped building, leaving “a mixture of totally destroyed, unstable but shorable and secure ruins”. In these ruins, Surrey established a Rescue School, where civil defence workers went to practise techniques for safely extricating victims from bombed-out structures. Squads came down from London and surrounding regions to become proficient in handling ropes and tackle, shoring up dodgy debris to allow workers access and victims egress, and extricating stretcher cases from precarious positions. The school’s superintendent, an engineer who in peacetime inspected the county’s bridges, routinely laid down in the rubble and observed students’ proficiency as they extricated him from beneath collapsed walls and out of cellar basements. Eric Claxton later admitted, “I pretended a nonchalance I did not always possess that their knots were properly formed when they hoisted me out from a third storey window opening! It made them more careful and gave them confidence to feel that I had faith in them”
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".