Cross-Border Insolvency a Need for India
Bibliographic record
Abstract
The present era of globalisation is integrating economies of countries into a global economic system and due to change in structural changes in the business there is an increase in the cross-border trade. Due to increased globalisation business of multinational companies has paved for business in multiples jurisdictions. Another challenge is due to Covid-19 pandemic many multinational Companies may go into bankruptcy, thereby increasing the chances of complex cross-border insolvencies. For enforcing an insolvency a company or creditor will need international agreements on cross border enforcement. The investor may invest in these multinational companies having assets and creditors in foreign nations, I case of insolvency of company, there will be conflict on insolvency and liquidation process. It is now when cross-border insolvency comes into play to protect the rights of domestic as well as foreign investors in case of corporate insolvency. In order to do common good and standardize International Trade Law in respect of Cross-Border Insolvency the United Nation Commission on International Trade Law was recommended in 1997 (“the Model Law”). The main aim of the Law is to, harmonize and implement unbiased legal framework of insolvency laws and process. The present study is restricted to Insolvency and Bankruptcy Laws of India, China, Australia, Canada, US and UK. The Governments today after globalization in order to protect the interests of creditors have enacted national laws for balancing both creditor as well as debtor benefits. Various developed countries are standardizing corporate insolvencies through cross-border insolvency, through intervention of government and regulatory mechanisms. The assets of corporate debtor are liquidated and distributed to secured creditors, unsecured creditors, employees, and all other stakeholders in a waterfall mechanism.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 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".