{"id":"W6949383138","doi":"10.5281/zenodo.14962027","title":"Generative Artificial Intelligence in Canadian Financial Services Marketing: Transforming Consumer Insights and Engagement","year":2025,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"AI in Service Interactions","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Humber Polytechnic","funders":"","keywords":"Transformative learning; Generative grammar; Financial services; Mainstream; Agency (philosophy); Workforce; Unbanked","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":[],"consensus_categories":[],"category_scores_codex":[0.005469493,0.0006081594,0.0002781105,0.002526514,0.01491075,0.01298227,0.001704463,0.002105564,0.01383743],"category_scores_gemma":[0.009440337,0.0003386556,0.00040458,0.004350672,0.01569906,0.003724988,0.006575391,0.003475308,0.001248867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1221329,"about_ca_system_score_gemma":0.1172497,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9555711,"about_ca_topic_score_gemma":0.9683508,"domain_scores_codex":[0.9949694,0.001650096,0.00008207311,0.0003177059,0.00210253,0.0008782139],"domain_scores_gemma":[0.9953393,0.001252668,0.0001672783,0.0002701046,0.001768355,0.001202177],"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":[0.00009467293,0.00006978914,0.005164525,0.0002321075,0.0000215491,0.0003696864,0.04141107,0.001763758,0.0005311518,0.6935708,0.07233185,0.184439],"study_design_scores_gemma":[0.00002798469,0.0000314154,0.008743349,0.0003386033,0.00001610333,0.0001570969,0.02803376,0.004627865,0.0006219784,0.1169583,0.8403415,0.0001021233],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.06568195,0.006980864,0.01257892,0.1316557,0.0005821808,0.0001793819,0.0003667089,0.000313901,0.7816604],"genre_scores_gemma":[0.9119536,0.007846083,0.01344359,0.006452906,0.0001308269,0.0001046169,0.0003762976,0.0002000841,0.05949203],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1221329,"threshold_uncertainty_score":0.8861402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0296158623090855,"score_gpt":0.2651378356079763,"score_spread":0.2355219732988908,"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."}}