Bibliographic record
Abstract
Judges have a dual role: they decide cases and they determine the law. These functions are conventionally understood to be intertwined: adjudication leads to case law, and disputes over judge-made laws lead to adjudication. Because judgments involve the resolution of past disputes, judge-made law is retrospective. The retrospective nature of judicial law-making can seem to work an injustice in hard cases. It appears unfair and inefficient for novel judicial decisions to apply to conduct occurring prior to the date judgment is handed down. A proposed solution is to separate the law-making and adjudicatory functions of courts. This is the technique of “prospective overruling”. Utilising this technique, courts seek to change the law prospectively for future cases, while continuing to decide past disputes under the “old” legal rule that was thought to apply at the time those disputes arose. This article challenges the claims that the exceptional juridical technique of prospective overruling is justified by values of stability, reliance, efficiency, dignity, and equality. These values, when properly understood, actually support rather than undermine the retrospectivity of judge-made law. Prospective overruling is an injudicious instrument.
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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".