Bibliographic record
Abstract
Misfeasance in public office is an intentional tort directed at conduct by public officials intended to harm a member of the public or carried out with an awareness of unlawfulness and the potential for such harm. This paper examines some of the mechanisms available to control expansion of this tort, with a focus on the mental element required. The Supreme Court’s 2003 decision in Odhavji v. Woodhouse held that it was sufficient to prove that the defendant was “subjectively reckless or wilfully blind”, but did not elaborate on those terms. While the Court made it clear that mere negligence is insufficient, the concept of recklessness may come close to that standard, raising potential concerns about the scope of the tort, given that there is no explicit basis upon which to negate its application on policy grounds, as there is under the law of negligence. However, the Court made it clear that “subjective recklessness” and “deliberate” conduct is still required. Other judicial statements of the need for a “stench of dishonesty” or “bad faith” provide an essential litmus test of what is actionable.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.031 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| 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".