Building Trust in Crisis Management: A Study of Insolvency Practitioners and the Role of Accounting Information and Processes
Bibliographic record
Abstract
ABSTRACT This paper seeks to understand how insolvency practitioners attempt to build trust with a heterogeneous creditor body during the crisis of formal insolvency and the role accounting information and processes play. Accounting information is mobilized in different ways according to how insolvency practitioners believe the information will be interpreted and valued. This paper suggests specific qualitative characteristics, accounting principles, and processes which appear to enhance trust building in a crisis context. These include perceived objectivity, comparability, cash flow accounting, “matching” of secured liabilities with secured assets, and “crisis” audit. The value ascribed by insolvency practitioners to maintaining specific creditor relationships also appears relevant to trust‐building activities. A “tit‐for‐tat” strategy emerges with secured creditors, whereby insolvency practitioners engage in demonstrable fee write‐offs, but on the implicit understanding that future, lucrative work will come their way. This study points to the importance for researchers and policymakers of understanding the “desirable” properties of accounting through informed understandings of how and why that information is mobilized and received in specific relationships between people.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".