Chapter 4: The Internet and Company Law
Bibliographic record
Abstract
The discussion that follows summarises and reviews some of the important developments and recent reports in the areas earlier identified as relevant to the Company Law Review's deliberations over information and communications technology (section 5.7 of the Strategic Framework Consultation Document). 1. Business Reporting on the Internet (refer to sections 3.2.1 and 4) Over the past four years there have been a number of online surveys of companies ' use of the internet to make available financial and other business information. Most of these were overviewed in the report submitted by the Centre for Business Research to the DTI in September 1999. Since then a further three broad surveys have been conducted and reported on by standard-setting bodies in recognition of the exploding use of the Internet for a business reporting and the dependence of users on this information for a wide variety of purposes, including online-trading, analyst investigations, academic research, e-commerce, etc. The first out was a report on a study commissioned by the International Accounting Standards Committee (IASC), entailing a survey of the Web reporting practices of 660 corporations in 22 countries 1. This has been the largest so far, both in terms of companies surveyed and the spread of the sample across countries. The second report was produced by the Canadian Institute of Chartered Accountants (CICA). It surveyed a sample of 370 companies from the 10,000 companies listed on the Toronto Stock
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".