<|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:"Understanding power positions in a new digital landscape: perceptions of Syrian refugees and data experts on relocation algorithm."
Abstract:"This study explores the differences and similarities between the perceptions of data experts and refugees as data subjects, in the context of a refugee relocation algorithm. The study conducted in-depth interviews with data experts and Syrian refugees in Estonia and Turkey. The results indicate that both refugees and data experts acknowledge the algorithms' potential power for structuring the everyday life experiences of people. Whereas refugees mainly focused on cultural and social concerns, the data experts underlined the importance of refugees' agency and the potential drawbacks of algorithms in terms of transparency and accountability. While both groups of interviewees thought the relocation algorithm could be useful especially in economic terms, the study demonstrates that algorithms create complex power relations and place extra pressure on both refugees and data experts. The new digital landscapes produced by algorithms entail a 'triple agency' - an agency of experts developing and using these datafied solutions, an agency of data subjects being targets of those calculations, and an agency of algorithms. For solving the issue of 'false authority', where the modelling of spatial choice cannot grasp the socio-cultural reality, it is necessary to consider the socio-cultural context of the calculative devices. A paradigm shift in machine learning is necessary from learning machines as autonomous subjects to machines learning from social contexts and individuals' experiences. Rather than experimenting with algorithmic solutions to speed up decisions about human lives, migration policies and relevant datafied solutions should consider the diversity of human experiences expressed in individuals' everyday life.."
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 = [1];
Q2 = [4,5];
Q3 = [];
Q4 = [0];
Q5 = [1];
Q6 = [0].
Understanding power positions in a new digital landscape: perceptions of Syrian refugees and data experts on relocation algorithm
This study explores the differences and similarities between the perceptions of data experts and refugees as data subjects, in the context of a refugee relocation algorithm. The study conducted in-depth interviews with data experts and Syrian refugees in Estonia and Turkey. The results indicate that both refugees and data experts acknowledge the algorithms' potential power for structuring the everyday life experiences of people. Whereas refugees mainly focused on cultural and social concerns, the data experts underlined the importance of refugees' agency and the potential drawbacks of algorithms in terms of transparency and accountability. While both groups of interviewees thought the relocation algorithm could be useful especially in economic terms, the study demonstrates that algorithms create complex power relations and place extra pressure on both refugees and data experts. The new digital landscapes produced by algorithms entail a 'triple agency' - an agency of experts developing and using these datafied solutions, an agency of data subjects being targets of those calculations, and an agency of algorithms. For solving the issue of 'false authority', where the modelling of spatial choice cannot grasp the socio-cultural reality, it is necessary to consider the socio-cultural context of the calculative devices. A paradigm shift in machine learning is necessary from learning machines as autonomous subjects to machines learning from social contexts and individuals' experiences. Rather than experimenting with algorithmic solutions to speed up decisions about human lives, migration policies and relevant datafied solutions should consider the diversity of human experiences expressed in individuals' everyday life.