{"id":"W4200371363","doi":"10.31235/osf.io/4nhd6","title":"Jumpstarting the Justice Disciplines: A Computational-Qualitative Approach to Collecting and Analyzing Text and Image Data in Criminology and Criminal Justice Studies","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg; University of Toronto","funders":"","keywords":"Computer science; Data science; Big data; Criminal justice; Context (archaeology); Field (mathematics); Process (computing); Qualitative property; World Wide Web; Data mining; Psychology; Machine learning; Criminology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.04841971,0.001022934,0.0009471893,0.008226261,0.01151261,0.01172573,0.004527363,0.002526982,0.006629465],"category_scores_gemma":[0.06863604,0.001086412,0.001219593,0.007507415,0.03414234,0.01167313,0.0114718,0.004407135,0.0009754447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01002106,"about_ca_system_score_gemma":0.01636882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01670237,"about_ca_topic_score_gemma":0.02762166,"domain_scores_codex":[0.938606,0.05402821,0.001027034,0.002302931,0.003213263,0.0008225768],"domain_scores_gemma":[0.9014173,0.08119234,0.003723417,0.007093275,0.005364252,0.001209445],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001008211,0.0002813099,0.005746464,0.001544436,0.0000604009,0.0005410144,0.3548401,0.002826784,0.001921833,0.548829,0.006875714,0.076432],"study_design_scores_gemma":[0.00008938654,0.0001300114,0.003258977,0.001795253,0.00005044433,0.0003089577,0.2578977,0.01254138,0.002262855,0.6069241,0.1146225,0.000118491],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04992945,0.001327181,0.8830431,0.02337331,0.0003448993,0.005184092,0.001450805,0.0002882842,0.03505889],"genre_scores_gemma":[0.3011125,0.0008962699,0.6779058,0.003795882,0.0001174978,0.0100385,0.0006582858,0.0002651254,0.005210199],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9515803,"threshold_uncertainty_score":0.2560709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4124329225740843,"score_gpt":0.5327767658549387,"score_spread":0.1203438432808545,"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."}}