{"id":"W3012760292","doi":"10.1007/s11129-022-09258-1","title":"Store expensiveness and consumer saving: Insights from a new decomposition of price dispersion","year":2022,"lang":"en","type":"article","venue":"Quantitative Marketing and Economics","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; University of Victoria","funders":"Mitacs; University of Cyprus","keywords":"Variance (accounting); Decomposition; Price dispersion; Work (physics); Dispersion (optics); Computer science; Advertising; Econometrics; Microeconomics; Business; Economics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004040616,0.0001383048,0.0002376627,0.0001669504,0.0003560358,0.0001043926,0.00008575078,0.00002784155,0.000180181],"category_scores_gemma":[0.000107176,0.0001524028,0.00003985129,0.0001076601,0.00006656881,0.0003956932,0.0003169908,0.0001162151,0.000003108066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002855132,"about_ca_system_score_gemma":0.00002198105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001864525,"about_ca_topic_score_gemma":0.0001576234,"domain_scores_codex":[0.9991922,0.00006991815,0.0002503645,0.0002944832,0.00006278684,0.0001302454],"domain_scores_gemma":[0.9989746,0.0005763853,0.0002734743,0.0001086241,0.00004627641,0.00002062837],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002368107,0.0001464422,0.9062092,0.0003242132,0.0001789147,0.00001425491,0.003525933,0.0002179602,0.01138961,0.01256491,0.001040581,0.06201983],"study_design_scores_gemma":[0.00189497,0.0000571367,0.9368794,0.000172054,0.0002346333,0.000007711645,0.0133124,0.01785866,0.0001401382,0.003396255,0.02538616,0.000660501],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967129,0.00136751,0.0001061585,0.0001398087,0.0002297337,0.0001377738,0.00001210246,0.00002400311,0.00127],"genre_scores_gemma":[0.9988122,0.0001440914,0.0006931024,0.0001719662,0.00005985072,0.000007674587,0.00004698142,0.00001788508,0.00004627261],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06135933,"threshold_uncertainty_score":0.6214807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02663476847200862,"score_gpt":0.2497432096387897,"score_spread":0.2231084411667811,"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."}}