Recovery from crises and lending
Bibliographic record
Abstract
During the recovery from the recent crisis, the general role of lending in economic growth, and particularly in the recovery from financial crises, has become an important issue. In this paper, we review the major differences between creditless recovery episodes and recoveries accompanied by growth of credit. Based on the literature, we find that creditless recoveries are relatively frequent phenomena: a quarter or one-fifth of all real economy recoveries take place without the growth of credit. The rate of economic growth is permanently lower during creditless recoveries than in episodes accompanied by credit expansion. Thus, lending activity of the financial intermediary system is usually necessary for fast recovery. When analysing the recovery from the current crisis, we find that a number of factors exist that predispose to creditless recovery. The current growth, the rate of which is lower than before the crisis, is taking place – both in the Member States of the European Union and in Hungary – with a decrease or stagnation of the credit stock. In the medium and long term, it is essential for the sustained growth of the real economy that loans granted by the financial intermediary system once again start to increase.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".