Who then â in Law â is my Neighbour? Lord Atkinâs âNeighbour Principleâ as an Aid for the Principled Delineation of the Boundaries of Negligent Liability
Bibliographic record
Abstract
In contemporary legal writing and discourse, Lord Atkin’s neighbour principle is unloved. The now dominant view is that the neighbour principle performs no practical function since it is a mere descriptive label of the very different factual circumstances in which a duty to take reasonable care exists. It is the central contention of this paper that the neighbour principle is – in fact – invaluable as aid for the principled development of the tort of negligence. As this paper will show, the neighbour principle furnishes a common perspective that renders possible uniform determinations of analogical similarity and difference between novel categories of relations and established forms of negligent liability. The principle thus works in tandem with analogical reasoning to ensure objectivity in the delineation of the proper ambit of negligence law’s protection. Accordingly, the principle is an essential in ensuring a principled law of negligence whereby like cases are treated alike.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".