Bibliographic record
Abstract
Precautionary measures are measures taken by the court to reform and rehabilitate the offender and prevent him from reoffending, taking into account the dangerous state. Although these measures were initially established to prevent crime and as an alternative to punishment, today they are a type of lenient punishment that is adopted and applied by the justice and judicial system to dangerous criminals after the crime has been committed. Given its dual nature (preventive and intimidatory aspects), preventive measures have both similarities and advantages with other social reactions. The Afghan legislator has foreseen provisions related to preventive measures in several articles of the former Penal Code and the Penal Code, and has addressed both aspects of preventive measures, and has considered the purpose of its creation to be the education and reform of the accused or convicted person, his readaptation to social life, and the prevention of the occurrence and repetition of crimes, taking into account the dangerous state. The scope of security measures is broad and includes a wide range of measures, from measures depriving of liberty to measures restricting freedom, measures depriving of rights and economic measures. The present study, using a descriptive-analytical method, has examined the definition and nature of security measures, their objectives, implementation conditions and cases from the perspective of the Afghan Criminal Code.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".