Bibliographic record
Abstract
A v National Blood Authority [2001] 241, 242-3, 244, 246, 250 Act for Prevention of Frauds and Perjuries (1677) (UK) 342 Administration Order (1881) (UK) 414 Advertising Commission (Netherlands) 123 advertising, misleading and unfair 2, 15, 18, 37, 107-28, 136-7, 201, 516 enforcement of advertising norms 122-8 government authorities, by 126-8 private action, by 125-6 self-regulation 122-5, 126, 549, 556 general laws against 108-13 basic elements of misleading or deceptive advertising 108-11 basic elements of unfair advertising 111-13 internet, and see under internet, ecommerce and consumer protection regulation of specific types of advertising 113-21 advertising to children 120-22 alcohol advertising 117-18 comparative advertising 113-15, 543 environmental claims 118-20 tobacco advertising 115-17 see also information and consumers Advertising Standards Authority (UK) 123 Advertising Standards Canada 124-5 advice to investors see under financial services regulation and investors Africa 47 agencies consumer see under consumer protection and safety enforcement by 485, 498-9, 509, 518-19, 534-5, 538-9, 541, 544-8 enforcement powers 549-50 enforcement styles 553-6 legislative styles 550-52 see also consumer goods; services sector; criminal law; regulation and regulators AGM case 40-41 Akerlof, G. 210 alcohol advertising 117-18 Alpa, G. 35 alternative dispute resolution 3, 476,
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.704 | 0.653 |
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".