{"id":"W2203301049","doi":"10.1093/oxfordhb/9780199935383.013.41","title":"Understanding the Distribution of Crime Victimization Using “British Crime Survey” Data","year":2015,"lang":"en","type":"book-chapter","venue":"Oxford University Press eBooks","topic":"Crime Patterns and Interventions","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Overdispersion; Conceptualization; Distribution (mathematics); Econometrics; Survey data collection; Criminology; Count data; Psychology; Computer science; Economics; Statistics; Mathematics; Poisson distribution; Artificial intelligence","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":[0.003149955,0.0002606811,0.000539502,0.002739004,0.0004207663,0.003042317,0.0008648245,0.0005538226,0.004316243],"category_scores_gemma":[0.01837611,0.0004655424,0.0002825487,0.003843342,0.00136321,0.003108493,0.0007557696,0.00116486,0.0005095772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002471278,"about_ca_system_score_gemma":0.0009853907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06613058,"about_ca_topic_score_gemma":0.06153441,"domain_scores_codex":[0.9986256,0.0007083108,0.00006391385,0.0001536759,0.0003896217,0.00005877355],"domain_scores_gemma":[0.9937826,0.005122632,0.0002082713,0.0003456071,0.0004836447,0.00005735823],"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":[0.00003969598,0.00002899686,0.05169036,0.0004146673,0.00007901922,0.0002820003,0.006280247,0.03755132,0.000776683,0.6059083,0.02977962,0.2671691],"study_design_scores_gemma":[0.000008090859,0.00003085542,0.07743197,0.0007021355,0.00002271221,0.0004051703,0.004324236,0.107189,0.0004562527,0.7188784,0.09047751,0.00007360039],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1996149,0.01710618,0.6657012,0.02112411,0.0003825579,0.0001756703,0.007961809,0.0007424014,0.08719122],"genre_scores_gemma":[0.8645811,0.01552521,0.09301958,0.001307146,0.0002965833,0.0001844568,0.007830649,0.0002547463,0.01700041],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06613058,"threshold_uncertainty_score":0.1314913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4316889134959208,"score_gpt":0.3476639632019911,"score_spread":0.08402495029392965,"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."}}