Bibliographic record
Abstract
Businesses and consumers alike face a problem of trust when making electronic payments over the Internet. The legal profession has so far failed to provide an answer to this problem largely due to the capability the Internet offers for instant advertising and selling products across legal jurisdictions. The UK is recognised as a key growth area for E-commerce yet the UK government have recently reduced the draft Electronic Communications bill, the ‘E-commerce bill’, from over 100 pages to around 6 (limited to tackling digital signatures). Is the UK moving to a model of selfregulation similar to that adopted in the US, or a temporary postponement designed to allow an infant industry to develop unrestricted. This article promotes an open self-regulating system structured around consumer choice and the establishment of ‘virtual legal zones’ through the mutual understanding of flexible guidelines, at least until sufficient case law can be developed.
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 | 0.031 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.095 |
| Scholarly communication | 0.027 | 0.025 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.022 | 0.023 |
| Insufficient payload (model declined to judge) | 0.006 | 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".