Non-Governmental Organizations Participation in Criminal Processes
Bibliographic record
Abstract
Despite their long-time physical presence in our country (Iran), non-governmental organizations (NGOs) have not been taken seriously by the government and public institutions, and play no effective role in criminal proceedings. An innovative by 2013 criminal procedure code is to realize doctrine of participatory criminal policy through NGOs participation in criminal proceedings, which has been provided for by legislator in Article 66 of mentioned code which was amended suddenly within a few days prior to being approved to come into effect on the basis of an interesting decision and which degraded NGOs’ right to litigate into the limit of that of indictors and viewers at proceedings. During proceedings, NGOs play the role of indictors and viewers, regardless of the lack of legal, cultural and social grounds necessary for them to take an active part in criminal proceedings in our country; and, in effect, they face such limitations and ambiguities as criteria of the recognition of their qualifications to do so (Article 66, provision 3). In addition, it is not clear how to develop NGOs’ participation in criminal proceedings and how to monitor their activities. Present study is intended to examine grounds of and barriers to NGO’s activity in criminal proceedings and to address vital roles they can play in the crime prevention and their involvement with criminal proceedings.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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 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".