<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are an expert on climate change, human mobility and migration. Answer the question truthfully.<|eot_id|><|start_header_id|>user<|end_header_id|>Here are the title and abstract of a scientific paper probably related to climate change, human mobility and migration..
Title:"Predicting Perceived Risk of Crime: a Multilevel Study."
Abstract:"With a sample of Italians selected from 71 Italian counties (N = 1,868), we performed two multilevel analyses aimed at predicting the perceived risk of crime at local (i.e., in the participants' county of residence) and at societal (i.e., in the context of Italian society) levels. A significant proportion of the variation in local risk perception was at the county level. The following individual variables predicted higher levels of this variable: indirect victimization, the perception of social and physical disorder, being a woman, being poorly educated, and being an older person. Among the ecological predictors, the crime rate and unemployment rate predicted higher levels of local crime risk perception, while the immigrant rate did not. Perceived risk of crime at the societal level did not show significant variation at the county level. Education, being a man, trusting people, and adhesion to post-materialistic values predicted lower levels of societal crime risk perception, while number of sons/daughters and exposure to television news increased it. The limitations and possible development of this study are discussed.."
I have six questions you need to answer.
Q1: Is this article is about 'human migration mobility': 1 = yes; 0 = no. Answer with a number. Do not give a textual explanation.

Q2: What drivers of migration and mobility does the article study? 0 = 'other', 1 = 'climate', 2 = 'environment', 3 = 'economic', 4 = 'conflict', 5 = 'political', 6 = 'social', 7 = 'family'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q3: What is the overall sentiment of the article?:0 = 'neutral', 1 = 'negative', 2 = 'positive'. Answer with a number. Do not give a textual explanation.

Q4: What type of climate or environmental keywords are explicitly mentioned?:  0 = 'none', 1 = 'algal blooms', 2 = 'infestation', 3 = 'infectious diseases', 4 = 'ocean acidification', 5 = 'sea ice', 6 = 'acid rain', 7 = 'drought', 8 = 'cold wave', 9 = 'heat wave', 10 = 'glacial', 11 = 'wildfire', 12 = 'air pollution', 13 = 'biodiversity loss', 14 = 'coastal erosion', 15 = 'land degradation', 16 = 'deforestation', 17 = 'desertification', 18 = 'water pollution', 19 = 'diebacks', 20 = 'permafrost loss', 21 = 'sand mining', 22 = 'sea level rise', 23 = 'earthquake', 24 = 'tsunami', 25 = 'volcanic', 26 = 'flood', 27 = 'avalanche', 28 = 'landslide', 29 = 'downburst', 30 = 'thunderstorm', 31 = 'sandstorm', 32 = 'haze', 33 = 'storm', 34 = 'cyclone', 35 = 'blizzard', 36 = 'hail', 37 = 'snow', 38 = 'tornado', 39 = 'wind', 40 = 'hurricane', 41 = 'typhoon', 42 = 'other'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q5: What discipline does the article belong to?: 0 = 'other', 1 = 'social science', 2 = 'natural science', 3 = 'arts and humanities', 4 = 'multidiscipline and interdiscipline'. Answer with a number. Do not give a textual explanation.

Q6: Does the article apply quantitative and/or statistical analysis?: 0 = 'no', 1 = 'yes'. Answer with a number. Do not give a textual explanation.

Use the following template: 

Q1 = [];
Q2 = [];
Q3 = [];
Q4 = [];
Q5 = [];
Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:
Q1 = [0];
Q2 = [];
Q3 = [];
Q4 = [];
Q5 = [];
Q6 = [1].
Predicting Perceived Risk of Crime: a Multilevel Study
With a sample of Italians selected from 71 Italian counties (N = 1,868), we performed two multilevel analyses aimed at predicting the perceived risk of crime at local (i.e., in the participants' county of residence) and at societal (i.e., in the context of Italian society) levels. A significant proportion of the variation in local risk perception was at the county level. The following individual variables predicted higher levels of this variable: indirect victimization, the perception of social and physical disorder, being a woman, being poorly educated, and being an older person. Among the ecological predictors, the crime rate and unemployment rate predicted higher levels of local crime risk perception, while the immigrant rate did not. Perceived risk of crime at the societal level did not show significant variation at the county level. Education, being a man, trusting people, and adhesion to post-materialistic values predicted lower levels of societal crime risk perception, while number of sons/daughters and exposure to television news increased it. The limitations and possible development of this study are discussed.