{"id":"W2262165990","doi":"10.1509/jmr.12.0518","title":"Capturing Context-Sensitive Information Usage in Choice Models via Mixtures of Information Archetypes","year":2016,"lang":"en","type":"article","venue":"Journal of Marketing Research","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; University of Saskatchewan","funders":"","keywords":"Bounded rationality; Context (archaeology); Computer science; Archetype; Conceptualization; Rationality; Perspective (graphical); Ecological rationality; Information integration; Data science; Artificial intelligence; Data mining; Epistemology","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":[0.01277155,0.001494471,0.001974209,0.003002884,0.001047012,0.005717658,0.002470432,0.002774003,0.00291717],"category_scores_gemma":[0.04755474,0.001502537,0.003794713,0.002998295,0.003516768,0.01148023,0.005595104,0.004021158,0.0004101811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002165845,"about_ca_system_score_gemma":0.001321524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002792244,"about_ca_topic_score_gemma":0.002064754,"domain_scores_codex":[0.9894109,0.006640234,0.0005393004,0.001282073,0.001638342,0.0004891467],"domain_scores_gemma":[0.9331192,0.05759129,0.003405315,0.004170294,0.001005181,0.0007088567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000420421,0.0002969009,0.008440961,0.0001835138,0.0003930725,0.0003096283,0.001577455,0.2191747,0.001986382,0.7299897,0.0003821269,0.03684519],"study_design_scores_gemma":[0.00002974815,0.0001027407,0.00133743,0.00003433051,0.00007842088,0.0001048347,0.000141957,0.6227134,0.0004731754,0.374078,0.0008335794,0.00007244801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08945514,0.000386441,0.9064131,0.0007652857,0.00002456316,0.0001045967,0.0001061494,0.0001217864,0.002622889],"genre_scores_gemma":[0.7951559,0.0004499724,0.2019072,0.0001581759,0.00005661916,0.00039504,0.0001525303,0.00005856337,0.001666068],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01277155,"threshold_uncertainty_score":0.06754327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1178245269798929,"score_gpt":0.2734879056652781,"score_spread":0.1556633786853852,"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."}}