?pote?e dayal? konut kredileri: Azerbaycan uygulamas?
Bibliographic record
Abstract
Bireyin en temel ihtiya?lar?ndan biri olan konut, ayn? zamanda bireyin sosyo-ekonomik faaliyetlerinde de etkin bir unsurdur. D?nyada ya?anan ekonomik, sosyal ve siyasal geli?meler, konut sorunun boyutlar?n? daha da farkl?la?t?rm?? ve yeni politikalar ve ??z?m ?nerileri ?retilmi?tir. ?potekli konut finansman? sistemi ?nemli ??z?m ?nerilerinden biridir. Bug?n geli?mi?lik d?zeyi farkl? bir?ok ?lkede, farkl? modellerle uygulanan ipotekli konut finansman? sistemini d?nyada en iyi uygulayan ?lkelere bak?ld???nda kurumsalla?m?? finansal yap?lar? ve istikrarl? ekonomileri dikkat ?ekmektedir. Mortgage sistemi denince akla ilk gelen ?lkeler ABD ve Avrupa Birli?i ?lkeleri, son derece geli?mi? konut piyasalar?yla, sistemin uygulamadaki halini g?rmek i?in b?y?te? alt?na al?nm??t?r. Bu ?al??mada, tarama y?ntemi kullan?larak konut finansman kaynaklar?, geli?mi? ve geli?mekte olan ?lkelerde uygulanan konut finansman modelleri ara?t?r?lm??, ipotek piyasas?nda kullan?lan kredi t?rleri, ipote?e dayal? menkul k?ymetler incelenmi?tir. Son olarak da Azerbaycan'da uygulanabilecek model ?zerine ?neriler ireli s?r?lm??t?r. One of the ost important requirements of an individual is a house. The house is also important in social-economic life of the individual. Economic, social and political developments on the world brought new solutions to housing problem. The Mortgage sytem is one of the most important solutions. Several models of mortgage is used in different countries depending on the development level of the country. The most successful mortgage systems are used in the USA and EU countries. In this project, using the documental detection model, the sources of housing finance, the house financing systems were researched in both developed countries and developing countries. At the end a research was made on Azerbaijan case.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.006 |
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".