{"id":"W4401554854","doi":"10.1051/itmconf/20246601003","title":"Customer Intelligence in the Cultural Sector: The Case of a Quebec Museum","year":2024,"lang":"en","type":"article","venue":"ITM Web of Conferences","topic":"Wine Industry and Tourism","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Business; Cultural intelligence; Knowledge management; Management; Computer science; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001704695,0.0003325979,0.000197967,0.001266893,0.02390307,0.007333355,0.001882708,0.002865444,0.007286897],"category_scores_gemma":[0.003553867,0.0002488735,0.0003185858,0.002481192,0.005031564,0.001731537,0.003059381,0.002476611,0.0005716507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05713428,"about_ca_system_score_gemma":0.04645885,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9774115,"about_ca_topic_score_gemma":0.9898999,"domain_scores_codex":[0.9981323,0.0005768182,0.00002481743,0.0000862639,0.0002913204,0.0008884579],"domain_scores_gemma":[0.9966245,0.0008281096,0.0001695398,0.000116829,0.00100011,0.001260967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0005332438,0.001047202,0.2005389,0.0004824426,0.0001470107,0.07093085,0.3862754,0.005476013,0.003995359,0.1149492,0.08793277,0.1276916],"study_design_scores_gemma":[0.00004014115,0.0001173354,0.08291347,0.000345566,0.00004697176,0.002222423,0.7265458,0.004243533,0.0005555375,0.001941369,0.1809293,0.00009855431],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.867024,0.001144276,0.0008804175,0.02167783,0.00008798548,0.0001716267,0.0002729397,0.00004932432,0.1086916],"genre_scores_gemma":[0.9809279,0.0004065894,0.0004515812,0.001339887,0.00001489967,0.00001904971,0.00006811004,0.00001604555,0.01675608],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05713428,"threshold_uncertainty_score":0.4145402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04306618136089309,"score_gpt":0.2779220628457453,"score_spread":0.2348558814848523,"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."}}