{"id":"W2802408029","doi":"10.1016/j.apgeog.2018.04.016","title":"Routine activity, population(s) and crime: Spatial heterogeneity and conflicting Propositions about the neighborhood crime-population link","year":2018,"lang":"en","type":"article","venue":"Applied Geography","topic":"Crime Patterns and Interventions","field":"Social Sciences","cited_by":72,"is_retracted":false,"has_abstract":false,"ca_institutions":"International Centre for Comparative Criminology; Université de Montréal","funders":"","keywords":"Census; Proposition; Census tract; Geography; Population; Geographically Weighted Regression; Criminology; Cartography; Demography; Sociology; Regional science; Statistics; Epistemology; Mathematics","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":[0.0138522,0.0006222397,0.002246082,0.005044091,0.001520767,0.006138284,0.003851428,0.002866375,0.005107343],"category_scores_gemma":[0.07253408,0.0008676515,0.001689296,0.006864642,0.01246454,0.00972724,0.006857968,0.002257564,0.0002659588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001696767,"about_ca_system_score_gemma":0.001953343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02307775,"about_ca_topic_score_gemma":0.01821013,"domain_scores_codex":[0.9879329,0.007478905,0.0006176311,0.002300526,0.001182454,0.0004877],"domain_scores_gemma":[0.8626181,0.1151979,0.009765668,0.008045025,0.003050348,0.00132301],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001113755,0.0006386059,0.6706653,0.001778051,0.004657762,0.0006140065,0.01174,0.01440525,0.0004324239,0.2120017,0.001753208,0.08019982],"study_design_scores_gemma":[0.00008917097,0.0002530119,0.6572856,0.0007522442,0.001806747,0.0004378267,0.02183372,0.01484693,0.0002935631,0.29988,0.002399381,0.0001217742],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9007885,0.01244352,0.03760754,0.02359353,0.0001742291,0.0001332827,0.001103773,0.00005751201,0.02409795],"genre_scores_gemma":[0.9953097,0.001666314,0.00208965,0.0003850225,0.0001357371,0.00005518439,0.0001276569,0.00001058586,0.0002201798],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02307775,"threshold_uncertainty_score":0.07325828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02550414180249791,"score_gpt":0.3405846199043447,"score_spread":0.3150804781018468,"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."}}