{"id":"W2185872490","doi":"10.1257/aer.20160999","title":"Mobilizing the Masses for Genocide","year":2020,"lang":"en","type":"article","venue":"American Economic Review","topic":"Political Conflict and Governance","field":"Social Sciences","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Genocide; Armed conflict; Exploit; Nexus (standard); Political science; Politics; Causality (physics); Political economy; Dirt; Development economics; Criminology; Demographic economics; Economics; Sociology; Law; Geography; Computer security; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002172828,0.0002260823,0.0002717569,0.001023574,0.0009959043,0.002471587,0.0003351791,0.0007817012,0.008255326],"category_scores_gemma":[0.006044972,0.0001549491,0.0001449302,0.000724017,0.001777559,0.001391315,0.002233496,0.0008409378,0.0008022697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008794433,"about_ca_system_score_gemma":0.0009540216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001751624,"about_ca_topic_score_gemma":0.003451048,"domain_scores_codex":[0.9986281,0.0007625939,0.00002594799,0.00008847589,0.0000923675,0.0004024286],"domain_scores_gemma":[0.9971334,0.0008350575,0.001302466,0.0002107045,0.0001466884,0.000371765],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0002363937,0.0005695527,0.270197,0.0007936384,0.0002407151,0.001167575,0.02506326,0.002293778,0.003216474,0.4150971,0.03246303,0.2486615],"study_design_scores_gemma":[0.0001480568,0.0005749787,0.5047826,0.001865054,0.0001559809,0.0007219231,0.05543464,0.002281275,0.00101915,0.09088992,0.3420791,0.00004736438],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8268183,0.006104167,0.00222084,0.01900965,0.0001760603,0.00007279588,0.0001604612,0.00001617452,0.1454216],"genre_scores_gemma":[0.9938087,0.002212466,0.0002126258,0.0005412854,0.00008772554,0.0000152864,0.00004169485,0.000003615627,0.003076544],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008255326,"threshold_uncertainty_score":0.02761686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07575143004864847,"score_gpt":0.3816587296890837,"score_spread":0.3059072996404352,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}