{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002569851,0.0003364256,0.000554038,0.001162834,0.0003683187,0.0010048,0.0009648264,0.0001815874,0.002738207],"category_scores_gemma":[0.0004079186,0.0003023011,0.0002147258,0.001103996,0.0003535547,0.00117835,0.000196999,0.0008578695,0.003081399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001387589,"about_ca_system_score_gemma":0.0001571631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002057935,"about_ca_topic_score_gemma":0.000215155,"domain_scores_codex":[0.9958656,0.0001890248,0.0009919194,0.0005469796,0.001611896,0.0007945639],"domain_scores_gemma":[0.9968825,0.0004120012,0.0005643066,0.0006109581,0.001386763,0.0001434492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009168342,0.0005007521,0.2429852,0.0002493265,0.0002700971,0.0000244339,0.0001738505,0.000003260887,0.2730332,0.0003423486,0.3161505,0.1653502],"study_design_scores_gemma":[0.002020878,0.00003509607,0.03281914,0.0002059418,0.000134511,0.00002009456,0.0008377938,0.00006543026,0.003430993,0.0001218296,0.9598688,0.0004395146],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9902612,0.002952746,0.0000557032,0.002679996,0.0006551868,0.0007637486,0.00004104997,0.00006503004,0.002525363],"genre_scores_gemma":[0.984122,0.0005669824,0.00007498217,0.0006711127,0.0001860391,0.00003727239,0.0000624003,0.00007998216,0.01419917],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6437182,"threshold_uncertainty_score":0.9999429,"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."}}