<i>The Snail and the Ginger Beer: The Singular Case of</i> Donoghue v Stevenson, Matthew Chapman (London: Wildy, Simmonds & Hill, 2010)
Bibliographic record
Abstract
When I first saw The Snail and the Ginger Beer, I was attracted more by its jacket (featuring a rather lively snail, tentacles extended) and title (most Holmesian, my dear Watson!) than by any expectation of what it might teach me about the case that heralded the modern law of negligence throughout the Commonwealth world. With all that has been written about Donoghue v Stevenson, could there be anything more to tell about the shocking case of gastroenteritis caused, it is said, by gastropod detritus lingering in some ginger beer? Well, yes, actually, and Matthew Chapman has done an excellent job in the telling. Chapman, a London barrister, has produced a well-researched and pithily presented story, not only of the case itself, but of the legal-historical context leading up to it and, to a lesser extent, its fate since being decided by a 3:2 majority of the House of Lords in 1932.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.015 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".