Bibliographic record
Abstract
Abstract COVID‐19 poses novel sources of uncertainty and risk to companies, but it also offers many opportunities. In the COVID‐19 era, unprecedented government and central bank interventions to tackle the economic crisis precipitated by the pandemic have reinvigorated the debate on the threat of a zombification of the economy in China, caused by unviable companies being kept alive artificially. This particular consequence of COVID‐19 may aggravate the economic problem of zombie companies in China, increase the risk of further zombification, and create new zombie companies. Recognizing the risk factors of zombie companies and revisiting corporate insolvency law in China, this article aims to address a gap in knowledge related to how zombie companies are being handled in practice in China in the era of the pandemic. In particular, we will investigate the definition, recognition, and uniqueness of zombie companies in the context of COVID‐19, and propose several policy actions, primarily through Chinese insolvency law, to mitigate the risk of the return of zombie companies or a further zombification of the economy. It is anticipated that these measures will help to enhance China's sustainable economic recovery in the wake of the COVID‐19 pandemic.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".