Bibliographic record
Abstract
During the past few years rescue culture flourished in the world. Many countries recently experienced a process of legislative reform and development in relation to their corporate rescue legislation. All of them tried to provide more chance for a company to overcome their financial difficulties through legislation. It has been believed that “assets would be more highly valued if utilized in the industry for which they were designed, rather than scrapped.” (Note 1) The Insolvency Act 1986 since it is applied had experienced a long history. However the effect of it in the area of corporate rescue is disappointed. The Enterprise Act 2002 marked a shift in direction for corporate insolvency law in the UK, which meant the UK had realized the weakness of the Insolvency Act 1986. This can be seen as the first step that the UK made to develop the rescue culture. However, this step is not long enough. In this article, firstly I will generally summarize the development of corporate rescue law in the Enterprise Act 2002. Then I will try to give my own proposal about what need to be further developed in the near future.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".