Government loan guarantees and the credit decision‐making structure
Bibliographic record
Abstract
Abstract Governments can alleviate the problem of banks denying credit to high risk borrowers and excluding weaker sectors from borrowing by introducing state‐guaranteed loan programs. The main contribution of this paper is the elucidation of the importance of the bank's credit decision‐making structure in ensuring overall effectiveness of loan guarantees. In particular, the government can use the guarantee as an instrument for credit inducement and for affecting the bank's decision‐making system, i.e., its degree of centralization, bias towards approval of loans and reliance on objective loan‐specific information. Résumé Les garanties gouvernementales de prêts et la structure de la prise de décision en ce qui a trait au crédit. Les gouvernements peuvent soulager le problème des banques qui refusent de faire crédit à des emprunteurs à hauts risques et excluent certains secteurs plus faibles d’accès au crédit, en introduisant des programmes de garantie de prêts. La contribution centrale de ce mémoire est l’élucidation de l’importance de la structure de prise de décision des banques en ce qui a trait au crédit pour assurer l’efficacité des garanties de prêts. En particulier, le gouvernement peut utiliser la garantie comme instrument d’incitation au crédit et pour influencer le système de prise de décision des banques, i.e., le degré de centralisation, la tendance à approuver les prêts, le recours à de l’information objective et spécifique au prêt.
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.
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 | 0.004 | 0.014 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".