Bibliographic record
Abstract
From the perspective of both the state and its citizens, it is vital that up‐to‐date versions of legislation relevant to an issue that concerns them are capable of being identified and accessed. If legislation is not readily and immediately accessible, finding it will prove to be a task that is beyond not only lay people but also competent and experienced lawyers. A principal cause of the difficulty encountered by users of statutes and statutory rules in finding the law on a particular topic that concerns them is that often the relevant provisions are to be found not in one self‐contained statute, but in a number of provisions scattered among a number of separate annual volumes. This article provides an overview of some historical and recent developments in the UK, Australia, New Zealand, Ireland, Jersey, and Canada, before proceeding to consider approaches by which responsible authorities keep their Statute Books accessible and coherent. It examines in detail the relative merits and demerits of the textual (or direct) method and the non‐textual (or indirect) methods of amendment. The article concludes that the benefits of having an up‐to‐date, accessible and coherent Statute Book must surely be obvious. Apart from the removal of the frustration, the cost savings to both the state and the private citizen in both time and effort are surely immense.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".