Bibliographic record
Abstract
Terrorism is a global threat.Terrorist acts such as murder, hostage taking, hijacking and destruction of property and places, violated the human rights and fundamental freedoms and affected international relations.In the Islamic criminal policy, resorting to terror, intimidation and general discomfort are crime and is associated with severe criminal response.At the same time, in the case of politically motivated violence to deal with the government or the ruling, the responses are regulative, defensive and in line with restoring social order.Global efforts to cope with terrorism began with the establishment of the League of Nations and with establishment of the United Nations and the spread of terrorism, especially in the second half of the twentieth century it was accelerated.This is a theoretical study and comparatively studies the accurate and reliable sources of relevant laws and rights in Iran.At first, using the library studies such as library research, study of various sources, including books, paper, student thesis, all the information and resources available in this field are collected and reviewed, and then they are reviewed and compared to the rules and regulations in Iran and Islam and the international rules regarding the subject of research.Then, comparing the comments, reviewing and criticizing a logical conclusion is obtained.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.975 | 0.975 |
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; the direct Gemma label and the distilled Codex classifier 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".