Pathology of Social Violence Phenomenon in Ardabil Province: A Qualitative Study
Bibliographic record
Abstract
INTRODUCTION: In all human societies, domestic violence is known as a threat. Violence is imposing one's will on others through mental pressure and physical damage then can cause a feeling of anxiety and insecurity in them, especially for the weaker and more vulnerable groups such as women, children, elderly and minority groups who are the victims of oppression and socioeconomic inequalities. According to statistics, Ardabil, in comparison with other Iran's provinces, has the most number of violent crimes. This qualitative research was conducted with the aim of exploring pathological phenomena of social violence in Ardabil province. METHODS: this qualitative study was conducted with expert panel. Eighteen participants were selected with targeted sampling method from professors and the heads of the administrative offices who were linked to the phenomenon of social violence and have rich experiences with the social violence issues. After obtaining an informed consent from the participants, expert panel were conducted in two sessions of 150 minutes. At each session all discourse was recorded and after that, immediately transcribed verbatim. Then, the codes, sub-themes and the themes were obtained. RESULTS: The five main extracted themes included: social, historical and anthropological, cultural, economic and regional factors and 13 sub-themes were classified. CONCLUSION: Social, economic, cultural and regional structure, which have been formed and institutionalized in the society over the years, can be influenced and changed by government policies and a variety of programs.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".