?pote?e dayal? konut kredileri: Azerbaycan uygulamas?
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 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