Enquête de victimisation, données suisses - 1996
Bibliographic record
Abstract
Co-ordinated by the Statistical Service of the Ministry of Justice of the Netherlands, this study aims at obtaining comparable data on crime in various countries. Since official statistics provide information that is heavily dependent on the organization of the police and the justice system in each country, the study's approach is to study the incidence of crime in the population - the degree of victimization in the population - by means of an international survey of the population. 14 countries participated in the 1989 survey: USA, Canada, Australia, France, England, Scotland, Northern Ireland, Spain, Federal Republic of Germany, Switzerland, Netherlands, Belgium, Norway and Finland, as well as two cities, Warsaw (Poland) and Surabaja (Indonesia). Japan participated on the basis of a somewhat modified questionnaire and sampling. The survey was resumed in 1992 in the following countries: England, the Netherlands, Belgium, Finland, USA, Canada, Australia, and additionally Sweden, Italy, New Zealand, Poland, Czech Republic, Slovakia, Georgia, Estonia, Indonesia and Costa Rica. On the other hand, Scotland, Northern Ireland, Germany, Switzerland, France, Norway, Spain and Japan didn't take part. Selected cities in the following countries also took part: Argentina, Albania, India, South Africa, Russia, Slovenia, Uganda, Brazil, Philippines, Egypt, Tanzania, Tunisia, China. The following crimes were investigated by the investigation: car theft, motorcycle theft, moped theft and bicycles theft, burglary, robbery, simple theft and pickpocketing, sexual assault, assault and battery, threats. Respondents who were victims of such crimes were asked a few brief questions about the place of the offense, the material consequences, the report to the police, the satisfaction with the police action, and the received assistance. All the interviewees were also asked to express themselves about their fear of crime, their satisfaction with the local police, their preventive attitude towards crime, how severely they would sentence a 21-year-old repeat burglar. Note that the questionnaire has evolved between successive surveys. After 1992, the survey was resumed twice at the international level and once at the Swiss level. In total, the following survey waves were completed: 1989 international survey (with Swiss participation) 1992 international survey (without Swiss participation) 1996 international survey (with Swiss participation) 1998 Swiss survey 2000 international survey (with Swiss participation)
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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".