The law and economics of lockdown mitigation: Bankruptcy errors in the <scp>United Kingdom</scp>
Bibliographic record
Abstract
Abstract The United Kingdom Government has undertaken unprecedented economic activity to support UK business during the COVID‐19 pandemic. This article applies the law and economics of corporate bankruptcy to these provisions. In particular, it examines whether legal responses to the pandemic encourage Type I bankruptcy errors (where a company that could be saved enters a terminal insolvency process) or Type II bankruptcy errors (where a company that could not be saved avoids a terminal insolvency process). Whilst more could undoubtedly have been done, it seems that the UK Government's actions to avoid Type I errors arising from the pandemic may have caused Type II errors. More pertinently, it is almost impossible for the UK Government to lift these protections in a neutral way – if all are uniformly lifted too soon, then this will result in Type I errors; if all are uniformly lifted too late, then this will result in Type II errors. It is impossible and undesirable to decide when to lift protections on a case‐by‐case basis, and any attempt to selectively lift protections results in the UK Government deciding which sectors have advantages post‐COVID‐19 and which do not. Accordingly, in setting and lifting legal protections, the UK Government finds itself a key market actor in deciding the post‐COVID‐19 shape of the UK market.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".