Bibliographic record
Abstract
Donoghue v Stevenson is justly the most well-known legal case, at least in Commonwealth legal systems, and its fame rests largely on the judicial opinion of Lord Atkin in the case and his enunciation of the neighbour principle, which heralded the modern law of negligence. Among Donoghue devotees, it is well known that May Donoghue’s counsel cited only seven cases in written argument — in contrast to the roughly two dozen cited by Lord Atkin. So even without Atkin’s cryptic modesty (I speak with little authority on this point, but my own research, such as it is . . . ), we can infer that he must have pursued his own research agenda. What has so far been an inference is now a certitude, with the startling discovery of a bundle of papers relating to the case. Written in a spidery and sometimes illegible hand and merely initialled JRA (that is, James Richard Atkin), they reveal Atkin’s research process, his innermost thoughts about the state of the law, his efforts to lobby his judicial colleagues, and his excitement as the judgment took shape. The Donoghue Diaries — transcribed by the author before being lost in a fire — are therefore a must read for legal historians, lawyers, jurisprudes and all aficionados of the legal imagination. Footnotes have been added for clarification or reference, and citations and other conventions have been updated where warranted.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.064 | 0.019 |
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".