Bibliographic record
Abstract
The effective trade credit and debt collection management is a problem that every company faces sooner or later. Pursuant to Euler Hermes, customer receivables usually account more than 40% of a company’s assets and one in ten invoices on average become overdue, many of which end up as unpaid bad debt. According to the estimates and expectations of Bulgarian National Bank for the first quarter of 2022 intercompany indebtedness in Bulgaria is growing, although at a slow pace, which is a prerequisite for the increasing of the bankruptcies number. Ciela Info reports show that the number of companies going bankrupt grows every year. The debtor companies are progressively rescheduling their payments and it is more and more difficult for them to repay their debts. As a result of the COVID crisis and the situation in Ukraine, even the largest companies in the country begin to suffocate and prolong the days of deferred payment. In order to continue to exist, businesses need to take only a well-measured trade risk, which is expressed in the granting of trade credits that do not negatively affect the operating result. This risk needs to be analysed, predicted and managed. The report represents the current state of the intercompany indebtedness in Bulgaria, identifying and analysing the factors that cause its escalation, as well as focusing on possible solutions to deal with the problem.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.024 |
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".