Bibliographic record
Abstract
In this article we try to identify potential sources of credit cyclicality. Unfortunately, contemporary models of the credit cycle are paying insuffiecent attention to this issue. The substitution of notions occurs quite often and reveals itself as a serious theoretical flaw. For example, in some studies the mechanism is accepted as the source of credit cyclicity. In some cases, factors of cyclical fluctuations are recognized as the main cause of such credit dynamics. Using the terminological approach, we carried out a comparative analysis of potential sources of the credit cycle, proposed in the literature on the issue. As the result of the study we offer to determine the source of the credit cycle as its basis - an element, without which changes in supply and demand for credit cannot be inherently cyclical in nature. In our opinion the sources of this phenomenon can be found in bounded rationality of lenders and borrowers, and in uncertainty of economic conditions. This approach allows us to determine the core of the credit cycle, as a mechanism of credit risk oscillations in the short-term and medium-term periods. This approach also allows us to successfully solve the theoretical controversy regarding the nature of the credit cycle, existing in the modern literature on the issue.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".