Bibliographic record
Abstract
This article makes three novel contributions to English private law jurisprudence. First, it identifies unifying principles within the limitation doctrine of discoverability that can aid the interpretation of several key provisions in the Limitation Act 1980. Secondly, it uses these principles to show how contradictions in the English courts’ approach to the discoverability of mistakes of law should be corrected. The article aims to reconcile lawyers’ understanding of discoverability across errors of fact and law. Thirdly, the article offers a new understanding of the maligned notion of a settled-view-of-the-law, which has so far failed to receive consensus among restitution jurists. The implications of the article’s thesis suggest that the landmark judgments in Kleinwort Benson Ltd v Lincoln CC, Deutsche Morgan Grenfell v IRC and FII Test Claimants v HMRC misapplied the discoverability provision in s.32(1)(c) of the Limitation Act 1980, erroneously exposing (in the overpaid tax cases) billions of pounds of past-collected revenue to litigation. The time is overdue for the English courts to correct their discoverability jurisprudence.
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 | 0.021 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.068 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".