Bibliographic record
Abstract
It is just over 52 years since I first got mixed up in IP.1 What I joined was on its face an essentially stable system. The Patents Act 1949 was bedded in. The Trade Marks Act 1938 was working fine. The Copyright Act 1956, with just 51 sections, underpinned copyright. Registered designs under the Registered Design Act 1949 caused no apparent trouble. (There weren’t many of them anyway.) There had been no new IP legislation since the 1956 Copyright Act. Competition law had nothing to do with IP. There was little litigation in the courts—though quite a lot of opposition work in the Patent Office (true opposition to grant2). Indeed, there was so little patent litigation that the single English Patent Judge, Lloyd Jacob J, in 1963 spoke of the ‘somewhat exiguous list of the assigned judge’.3 There was but little counterfeiting. Such as there was was not of fashion items or perfumes, but of pharmaceuticals. Foreign IP of all sorts was completely irrelevant. We knew nothing of it. Not that of Commonwealth countries such as India, Canada or Australia, even though their laws were largely derived from UK law, nor that of the USA—where patents were largely irrelevant because the courts largely held them invalid, nor Continental Europe where each country did its own thing without regard to another.
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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.021 | 0.028 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.035 | 0.007 |
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".