{"id":"W4362637646","doi":"10.1016/j.jbusres.2023.113887","title":"When injured product users may also stay satisfied: A macro-level analysis","year":2023,"lang":"en","type":"article","venue":"Journal of Business Research","topic":"Safety Warnings and Signage","field":"Psychology","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Social Sciences and Humanities Research Council of Canada; McMaster University","keywords":"Leverage (statistics); Harm; Product (mathematics); Macro; Macro level; Customer satisfaction; Business; Marketing; Psychology; Economics; Computer science; Social psychology; Mathematics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006446435,0.0001866415,0.0006075418,0.003193477,0.0002542163,0.0001660259,0.0007730552,0.0001399646,0.005531824],"category_scores_gemma":[0.001158099,0.0001486674,0.0002700737,0.008350763,0.0002003093,0.000259056,0.0002124066,0.0009767064,0.0005459954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001164394,"about_ca_system_score_gemma":0.0002867947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008117685,"about_ca_topic_score_gemma":0.0001166963,"domain_scores_codex":[0.9955454,0.0008117597,0.0007720399,0.0003929589,0.001643125,0.0008346711],"domain_scores_gemma":[0.995993,0.0005583974,0.0003734618,0.000607005,0.002240686,0.0002274729],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.002481139,0.0005609596,0.2606566,0.0001689374,0.005862424,0.003096571,0.01434519,0.001069659,0.01672385,0.001076485,0.618311,0.07564722],"study_design_scores_gemma":[0.0009133379,0.0001286093,0.9467955,0.00004632443,0.000167006,0.00006037988,0.001526377,0.00007203233,0.0001840783,0.0006827118,0.04925192,0.0001716727],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9749088,0.0006994559,0.0009345685,0.01822499,0.0008915712,0.0003290242,0.00004939053,0.00005461323,0.003907563],"genre_scores_gemma":[0.9792461,0.0002558402,0.0007199035,0.00009603137,0.0006950237,0.00001706194,0.00001839193,0.00005001383,0.01890165],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.686139,"threshold_uncertainty_score":0.9953772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1980716929538726,"score_gpt":0.4485166684815742,"score_spread":0.2504449755277016,"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."}}