{"id":"W2118059401","doi":"10.1287/mksc.1120.0723","title":"Can Brand Extension Signal Product Quality?","year":2012,"lang":"en","type":"article","venue":"Marketing Science","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Argument (complex analysis); Product (mathematics); Quality (philosophy); Brand extension; Pooling; Extension (predicate logic); Economics; Microeconomics; Bayesian game; Observability; Mathematical economics; Computer science; Econometrics; Marketing; Mathematics; Brand awareness; Business; Game theory; Repeated game","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":[],"consensus_categories":[],"category_scores_codex":[0.01117546,0.0001485244,0.0001470096,0.0002722721,0.0006966681,0.0004281909,0.0003794204,0.00002262813,0.0004354292],"category_scores_gemma":[0.001519272,0.0001309469,0.000047207,0.001173354,0.0002612351,0.00168485,0.0003216992,0.0001360946,0.00008337417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000032082,"about_ca_system_score_gemma":0.00004816935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006159021,"about_ca_topic_score_gemma":0.0000445497,"domain_scores_codex":[0.998054,0.00004956979,0.0002510414,0.000396936,0.0005874775,0.0006609613],"domain_scores_gemma":[0.999102,0.0001729328,0.0001613252,0.0003272196,0.000197619,0.00003890956],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005399955,0.00005329157,0.8159886,0.00008293219,0.000002006773,0.000001955444,0.00009133991,0.000002199172,0.06022307,0.0006406757,0.0007089573,0.122151],"study_design_scores_gemma":[0.0001549202,0.000001840453,0.9866664,0.00005587604,0.0000221963,0.000005014635,0.0001274854,0.0002833767,0.0005627288,0.00007355118,0.0117913,0.0002552708],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9582236,0.0001535877,0.0000403111,0.0007934608,0.0007592403,0.0001580639,5.62296e-7,0.0001309033,0.03974021],"genre_scores_gemma":[0.9977786,0.000002291805,0.0002674214,0.0006758184,0.0008269947,0.000006490382,0.000002066845,0.00001368524,0.0004266328],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1706778,"threshold_uncertainty_score":0.5358278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.036149061105759,"score_gpt":0.2813675028195449,"score_spread":0.2452184417137859,"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."}}