Bibliographic record
Abstract
This article on policing and crime in Israel reflects the complex heterogeneous nature of the state of Israel as well as its police. The two largest minority communities in Israel are the Israeli Arabs (c. 21 percent) and the ultra-Orthodox Jews (c. 10 percent). Another group, although much smaller in size (c. 2 percent), is the Ethiopians. Due to Israel’s geopolitical and national history, the Arabs are perceived as a security threat by the public as well as by the police. Consequently, much of the research about minority policing focuses on policing the Arab society. Ethiopians are subject to similar discriminatory policing as the Arabs, but in their case the police are making greater efforts to rectify the situation. The ultra-Orthodox community has been responsible for most of the mass clashes with the state since its early days, despite or maybe due to the persistent ideological tension against the existence of the state that is accompanied with a growing integration into Israeli society. Israeli policing, which has received very little scholarly attention to date, has some similarities to and differences from police forces elsewhere. Similarities are seen in the steadily increasing number of women in police force (28 percent) as well as the number of women officers in senior positions. In addition, as in other police forces, there is growing success in recruitment of police officers from minority groups, especially Arabs, ultra-Orthodox, and Ethiopians. Yet, unlike other countries, the number of volunteer and military police officers in the Israeli police is alarmingly high, as such police officers tend to be older and tend to function according to military rather than civilian norms. Military service in the Israeli Defense Forces inevitably affects the way officers perceive Arab citizens, and it contributes to stress, burnout, and trauma. Moreover, the police are forbidden from striking, which affects employment conditions, the quality of the workforce, and morale.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".