{"id":"W4399322401","doi":"10.1057/s41270-024-00330-1","title":"The digital divide: psychographic segmentation in the Canadian banking context","year":2024,"lang":"en","type":"article","venue":"Journal of Marketing Analytics","topic":"Cultural Differences and Values","field":"Psychology","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Psychographic; Context (archaeology); Segmentation; Digital divide; Business; Computer science; Advertising; Geography; Artificial intelligence; World Wide Web; The Internet","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003483148,0.00008309346,0.0001044398,0.0001652168,0.0002089153,0.0008158732,0.0002682688,0.0000463553,0.0001062911],"category_scores_gemma":[0.0003107776,0.00003990758,0.0001427302,0.0003788228,0.00005777286,0.0001004875,0.000008616512,0.0003956053,0.00001144537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006510133,"about_ca_system_score_gemma":0.00006308797,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002050258,"about_ca_topic_score_gemma":0.04723273,"domain_scores_codex":[0.9987457,0.0003343059,0.0003718808,0.00008711899,0.0002547161,0.0002062708],"domain_scores_gemma":[0.9982535,0.00138253,0.0001354352,0.0001049231,0.00006808885,0.00005557276],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002335831,0.00006510315,0.3377784,0.00004394335,0.0006002228,0.0003843931,0.01401898,0.00004651381,0.00002639759,0.006903502,0.09506715,0.5448319],"study_design_scores_gemma":[0.0003649556,0.0001304119,0.8921632,0.0004727299,0.0001300022,0.0003214155,0.02577439,0.001529542,9.866837e-7,0.003562931,0.07538453,0.0001649037],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9693326,0.00404401,0.00005344197,0.008717396,0.001361324,0.00008877063,0.000007440621,0.000008006642,0.01638702],"genre_scores_gemma":[0.9986974,0.0001529469,0.000009940675,0.0002612115,0.0002924734,0.000001328175,0.000001763704,0.000006709753,0.0005762443],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5543848,"threshold_uncertainty_score":0.9701528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06112372675550171,"score_gpt":0.3568318894321795,"score_spread":0.2957081626766778,"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."}}