{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01202701,0.0004258074,0.0006656557,0.0004364327,0.0005607231,0.002422603,0.0006176811,0.001101812,0.004068951],"category_scores_gemma":[0.06036517,0.0006766707,0.0003299227,0.0003749706,0.001776097,0.002272178,0.001272748,0.00181581,0.0005883494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001066263,"about_ca_system_score_gemma":0.001577604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002173602,"about_ca_topic_score_gemma":0.001272315,"domain_scores_codex":[0.9926341,0.004258774,0.0002821437,0.000575438,0.001944116,0.000305461],"domain_scores_gemma":[0.9001198,0.08003236,0.007633241,0.007707065,0.002811541,0.001696062],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.01284114,0.004866985,0.1261202,0.001690593,0.000477179,0.000654766,0.02238507,0.02472946,0.3527723,0.1850079,0.003147411,0.2653069],"study_design_scores_gemma":[0.002387559,0.004829639,0.1862942,0.0004511795,0.0007322622,0.000933613,0.003327882,0.2420155,0.1523491,0.3805668,0.02551774,0.000594552],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9370032,0.0004623285,0.04251191,0.000573999,0.00004031679,0.0002414606,0.00005683178,0.0001920122,0.01891786],"genre_scores_gemma":[0.9811738,0.0001839027,0.01726666,0.0001548117,0.00001749334,0.00009234015,0.00004445804,0.00005262784,0.001013956],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01202701,"threshold_uncertainty_score":0.06360567,"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."}}