Междуфирмената задлъжнялост в България – проблеми и възможни решения
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it