{"id":"W2064661564","doi":"10.1002/mar.20162","title":"Developing heuristic‐based quality judgments: Blocking in consumer choice","year":2007,"lang":"en","type":"article","venue":"Psychology and Marketing","topic":"Decision-Making and Behavioral Economics","field":"Decision Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Heuristics; Quality (philosophy); Blocking (statistics); Affect (linguistics); Product (mathematics); Order (exchange); Heuristic; Psychology; Process (computing); Advertising; Block (permutation group theory); Marketing; Information quality; Computer science; Business; Information system; Artificial intelligence; Communication","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02377548,0.0001513085,0.0003288448,0.0003873709,0.0001935986,0.0001097641,0.0003479756,0.0001644773,0.0001628283],"category_scores_gemma":[0.01038371,0.0001333831,0.0000522183,0.0004384209,0.000148023,0.0001291457,0.0001095729,0.0002751835,0.00005933123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004230925,"about_ca_system_score_gemma":0.00004005399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004397631,"about_ca_topic_score_gemma":0.0002952041,"domain_scores_codex":[0.9970332,0.0005108987,0.001079004,0.0006674828,0.000292759,0.0004167009],"domain_scores_gemma":[0.9895622,0.009524605,0.0003357325,0.000373548,0.0001088915,0.00009501054],"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":[0.0001462334,0.00003684893,0.445442,0.000003342385,0.00000292953,0.00001841608,0.00004686423,0.000003729068,0.0001392287,0.0000923818,0.0002961375,0.5537719],"study_design_scores_gemma":[0.0008959456,0.00001712068,0.9527298,0.00007771709,0.000005907074,0.00001647232,0.0002593223,0.0001500824,0.00004886336,0.01114233,0.03442289,0.0002335341],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9776834,0.0002507019,0.01118716,0.0008212523,0.0007340422,0.0001003082,0.000003073642,0.00003109683,0.009188972],"genre_scores_gemma":[0.9883347,0.00002352774,0.009231634,0.002262899,0.00005241207,0.000003848206,0.000001544014,0.000009841843,0.00007963047],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5535383,"threshold_uncertainty_score":0.9979522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2684398167341324,"score_gpt":0.5225066344660648,"score_spread":0.2540668177319324,"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."}}