Hisseli Konut Mülkiyet Modeli: Alternatif Mülkiyet Yönetimi ve Küresel Uygulanabilirlik
Bibliographic record
Abstract
Küresel konut piyasasında yaşanan finansal zorluklar, alternatif mülkiyet modellerine duyulan ihtiyacı artırmıştır. Dr. Semih Artün tarafından geliştirilen Hisseli Konut Mülkiyet Modeli (HKMM), bir konutun birden fazla yatırımcı tarafından hisseler halinde satın alınmasını sağlayarak hem konut sahipliğini yaygınlaştırmayı hem de yatırım verimliliğini artırmayı hedeflemektedir. HKMM, hissedar bazlı mülkiyet paylaşımı, zaman bazlı kullanım yönetimi ve hukuki güvence altına alınmış devir mekanizması gibi unsurlarla geleneksel mülkiyet modellerinden ayrışmaktadır. Model, yatırımcıların finansal esnekliklerini artırarak konut piyasasına daha kolay giriş yapmalarına olanak tanımaktadır. Bu çalışmada, HKMM’nin hukuki, finansal ve operasyonel çerçevesi ele alınarak, farklı ülkelerdeki uygulanabilirliği değerlendirilecektir.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".