<|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:"Revisiting the Effects of Case Reports in the News."
Abstract:"Synthesizing several theories about the likely impact of case reports in the news, we propose that the impact of featuring identified victims in a news story is contingent on the degree of similarity between the audience member and the identified victims. We execute a population-based survey experiment involving immigration policy to examine our theory. Our results suggest that featuring specific, identified victims in a news story will promote more supportive policy opinions than otherwise identical stories about unidentified victims, but only when the victim is highly similar to the audience member. Conversely, case reports featuring identified people who are dissimilar to the audience member will decrease the extent to which the story encourages victim-supportive policy attitudes. Overall, our experimental findings shed light on the conditions under which the inclusion of case reports increases versus decreases the policy relevance of news stories. Our findings also help explain previous inconsistencies in findings about the impact of case reports. Additional analyses allow us to speculate as to the reasons for the differential direction of effects.."
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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 = [1];
Q2 = [0];
Q3 = [0];
Q4 = [0];
Q5 = [];
Q6 = [1].
Revisiting the Effects of Case Reports in the News
Synthesizing several theories about the likely impact of case reports in the news, we propose that the impact of featuring identified victims in a news story is contingent on the degree of similarity between the audience member and the identified victims. We execute a population-based survey experiment involving immigration policy to examine our theory. Our results suggest that featuring specific, identified victims in a news story will promote more supportive policy opinions than otherwise identical stories about unidentified victims, but only when the victim is highly similar to the audience member. Conversely, case reports featuring identified people who are dissimilar to the audience member will decrease the extent to which the story encourages victim-supportive policy attitudes. Overall, our experimental findings shed light on the conditions under which the inclusion of case reports increases versus decreases the policy relevance of news stories. Our findings also help explain previous inconsistencies in findings about the impact of case reports. Additional analyses allow us to speculate as to the reasons for the differential direction of effects.