Structured Dataset of Egyptian Drug-Related Criminal Cases
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
This dataset contains structured legal data extracted from multiple Egyptian legal references, primarily focused on drug-related criminal cases. The sources include the books "مجموعة الأحكام الصادرة من الهيئة العامة للمواد الجنائية ومن الدوائر الجنائية" (Collection of Judgments Issued by the General Authority for Criminal Matters and Criminal Circuits) and "الموسوعة الذهبية في قضايا المخدرات" (The Golden Encyclopedia of Drug Cases), both of which are publicly available and government-authorized. The dataset includes approximately 200 entries and 9 columns, covering key fields such as charges, case facts, legal reasoning, applicable laws, judgment outcomes, prison terms, fines, and more. Data was extracted using a combination of manual transcription and OCR techniques, and then formatted into a structured CSV file. This resource is designed for researchers and NLP engineers working in Arabic legal text processing. It supports various tasks including classification, prediction (e.g., law prediction based on facts), legal reasoning analysis, and fine-tuning Arabic legal NLP models. Use Restrictions: This dataset is intended strictly for academic and research purposes. It must not be redistributed for commercial use or re-published under another name. Any use must acknowledge the original source and contributors.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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