FEATURES OF CRISIS MANAGEMENT IN INTERNATIONAL PRACTICE
Bibliographic record
Abstract
The article discusses some fundamental mechanisms of crisis management: managing the insolvency and bankruptcy processes of international organizations. As an illustrative example, the Canadian experience as a successor system aimed at preserving the debtor organization and, if possible, restructuring the debt to restore the company’s solvency and financial stability. It characterizes the activities of the Office of the Superintendent of Bankruptcy (OSB) and trustee in bankruptcy (“trustees”). The Bankruptcy and Insolvency Act (“BIA”) and the key officials’ responsibilities in the framework of crisis measures are analyzed. Differences are identified between the voluntary bankruptcy and forced bankruptcy. In the Canadian crisis management practice, insolvency is not identical to bankruptcy for both individuals and legal entities. Moreover, the very concept of “person” has an extended interpretation. The successor system involves the search for grounds for the bankruptcy exemptions, especially for individuals who do not cease to exist as legal entities. Therefore in the Canadian crisis management practice, the provision on mandatory financial and tax advice to individuals who have become bankrupt for the first time seems extremely interesting. Conclusions about the possibility of using some individual elements of the Canadian crisis management system in Russian practice are drawn.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.029 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".