Bibliographic record
Abstract
Everyone makes mistakes or acts carelessly. Most of the time, the error is harmless and life goes on much as normal. But, at other times, a small slip can result in large and harmful consequences; injuries can be widespread and damage can be catastrophic. While the common law has long had a set of rules that holds careless individuals responsible for their negligent acts, it continues to wrestle with the thorny question of whether they should be liable for all or only some of the damage that they cause. For example, someone might inadvertently drop a still-lit cigarette stub in a waste bin that results in a fire that burns down a whole district and kills many people. In legal doctrine, this is known as ‘the remoteness of damage’ problem. For many, this distinction between the nature of the act done and the extent of damage caused by it may appear contrived and almost beside the point. It might be claimed that people should have a moral obligation to assume the full costs and consequences of their blameworthy actions, even if the extent of damage was unexpected; the law should effect and mirror such a stance. However, for the last fifty years or so, the common law has taken seriously the argument that there should be some proportionality between the nature of the careless act and the extent of liability for its consequences; small acts of negligence should give rise to smaller liability than larger ones. As with much else, ‘reasonableness’ is the watchword of the common law of tort. The leading cases on remoteness of damage, each flowing from the same shipping incident in Sydney Harbour, occurred in the mid-twentieth century. Like so many of the common law's great cases, the surrounding circumstances and characters involved as well as the social attitudes displayed were very much in line with the times. However, since then, values and sensitivities have changed. Even though the guiding principles of law remain the same today, it is not too difficult to appreciate that a similar set of facts might well produce a different outcome today. And perhaps that is as it should be – the common law shifts and switches with the prevailing social milieu; it moves, not in lock step, but along much the same path, often lagging behind, but occasionally pulling ahead.
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 | 0.002 | 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 | 0.005 | 0.023 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".