{"id":"W2541438575","doi":"10.1080/17450101.2016.1211823","title":"Don’t shoot! Black mobilities in American gunscapes","year":2016,"lang":"en","type":"article","venue":"Mobilities","topic":"Crime, Deviance, and Social Control","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Mobilities; Gaze; Sociology; Computer science; Artificial intelligence; Social science","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":[0.001126027,0.0002280496,0.0002116869,0.001259073,0.008245409,0.003175611,0.0004695585,0.0007371567,0.005733981],"category_scores_gemma":[0.002301408,0.000220828,0.0001165039,0.0009616797,0.008098302,0.002412516,0.004698905,0.001693873,0.0002513401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001830374,"about_ca_system_score_gemma":0.0009468609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03036552,"about_ca_topic_score_gemma":0.05335431,"domain_scores_codex":[0.9992391,0.0003360408,0.00001708072,0.0001019018,0.0001121939,0.0001937225],"domain_scores_gemma":[0.999298,0.0001985426,0.0002503359,0.00004144833,0.00008579378,0.0001260136],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00006565765,0.00005613758,0.01869121,0.00004777201,0.000007209325,0.0003827763,0.9414907,0.00002999594,0.0008649582,0.02240974,0.00153493,0.014419],"study_design_scores_gemma":[0.000003264079,0.00002072328,0.02501037,0.00008591254,0.000006265582,0.0001967516,0.9577487,0.00005719839,0.0002593585,0.002263007,0.01433513,0.00001338665],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9665894,0.0002514881,0.0003711809,0.001761373,0.00003908756,0.000007332362,0.00001257521,0.000005198303,0.03096246],"genre_scores_gemma":[0.99739,0.0001214761,0.00008076816,0.0001748606,0.000009904338,0.000005226327,0.000007008742,0.000006113722,0.002204742],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03036552,"threshold_uncertainty_score":0.06037754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01568046498612549,"score_gpt":0.2994382147344972,"score_spread":0.2837577497483716,"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."}}