SUD AMALIYOTI MA'LUMOTLAR BAZALARINING HUQUQIY TAHLILDAGI AHAMIYATI.
Bibliographic record
Abstract
Ushbu maqola sud amaliyoti ma’lumotlar bazalarining huquqiy tahlil jarayonidagi o‘rni, afzalliklari va muammolarini keng yoritadi. Raqamli texnologiyalar davrida huquqshunoslik fanida Legal Tech yo‘nalishining jadal rivojlanishi sud amaliyoti ma’lumotlarini tahlil qilishda yangi imkoniyatlar yaratmoqda. Sud qarorlarining ochiqligi, ularni tizimlashtirish, tahlil qilish va natijalarini solishtirish orqali adolatli qarorlar chiqarish imkoniyatlari kengaymoqda. Maqolada O‘zbekiston Respublikasi sud amaliyoti bazalari tahlil qilinadi va ular AQSh, Buyuk Britaniya, Estoniya, hamda Rossiya tajribalari bilan qiyoslanadi. Tadqiqotning maqsadi — sud amaliyoti ma’lumotlar bazalarining huquqiy tahlil sifatini oshirishdagi o‘rnini aniqlash, mavjud kamchiliklarni aniqlash va ularni bartaraf etish yo‘llarini taklif etishdir
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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.013 |
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; both teacher heads agree on what is shown here.
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".