{"id":"W2945633278","doi":"10.1093/jcr/ucz019","title":"Lead by Example? Custom-Made Examples Created by Close Others Lead Consumers to Make Dissimilar Choices","year":2019,"lang":"en","type":"article","venue":"Journal of Consumer Research","topic":"Consumer Behavior in Brand Consumption and Identification","field":"Business, Management and Accounting","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Uniqueness; Product (mathematics); Personalization; Order (exchange); Lead (geology); Marketing; Inference; Social network (sociolinguistics); Business; Computer science; Internet privacy; Advertising; Microeconomics; Economics; Psychology; Social psychology; World Wide Web; Social media; Mathematics; 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":[0.002095955,0.0002083943,0.0002206815,0.0003135348,0.0008685651,0.002335949,0.0003373736,0.001017653,0.01076897],"category_scores_gemma":[0.01407894,0.0002455839,0.0003035207,0.0002721193,0.000968184,0.002308538,0.001297271,0.00105263,0.0005642231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003824598,"about_ca_system_score_gemma":0.0002279663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008800136,"about_ca_topic_score_gemma":0.001866477,"domain_scores_codex":[0.9984863,0.0009116876,0.00006856169,0.0001632668,0.000278839,0.00009128711],"domain_scores_gemma":[0.9896409,0.006798264,0.001584841,0.001029716,0.0004782271,0.0004680158],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002913529,0.002870061,0.6782266,0.0009835713,0.0005477744,0.00305103,0.07431263,0.002206053,0.02523508,0.03854762,0.006412146,0.1646939],"study_design_scores_gemma":[0.0003641641,0.001574663,0.7321418,0.0006907938,0.001010415,0.004807074,0.1212803,0.01730095,0.01882147,0.05058205,0.05105131,0.0003749972],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9869646,0.00008948518,0.0008663708,0.000291739,0.00001432789,0.00001307634,0.00001501739,0.00001199985,0.01173343],"genre_scores_gemma":[0.9971989,0.00006625826,0.001052919,0.0001702038,0.000005743185,0.000009900836,0.0000227966,0.000008498034,0.001464838],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01076897,"threshold_uncertainty_score":0.03602582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08631711099978763,"score_gpt":0.3501022999547401,"score_spread":0.2637851889549525,"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."}}