{"id":"W3046340444","doi":"10.3390/designs4030029","title":"An Enriched Customer Journey Map: How to Construct and Visualize a Global Portrait of Both Lived and Perceived Users’ Experiences?","year":2020,"lang":"en","type":"article","venue":"Designs","topic":"Persona Design and Applications","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Persona; Construct (python library); Computer science; Context (archaeology); Human–computer interaction; Cluster analysis; Portrait; Unconscious mind; Data collection; User experience design; Data science; Psychology; Artificial intelligence","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":[0.000119578,0.0001447628,0.0002128207,0.00004086036,0.0001038569,0.0001962581,0.000417854,0.00005447112,0.00002141747],"category_scores_gemma":[0.00003284757,0.0001354823,0.0000315794,0.0003589234,0.0001475129,0.0003335176,0.00008544402,0.0000619416,0.000007249434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001680505,"about_ca_system_score_gemma":0.00008781294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003159111,"about_ca_topic_score_gemma":0.000003471746,"domain_scores_codex":[0.9989023,0.00008550865,0.0001579024,0.0004411048,0.0001981708,0.0002150107],"domain_scores_gemma":[0.999126,0.00004297682,0.00008257593,0.0002345548,0.00005618782,0.0004577232],"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":[0.00005899777,0.0001527492,0.008356621,0.00004628324,0.00005750642,0.00004443128,0.1554401,0.000007003947,0.7865182,0.03193866,0.005915138,0.01146442],"study_design_scores_gemma":[0.009336085,0.006328753,0.498357,0.000226057,0.0002754631,0.0008804564,0.1451382,0.270581,0.02498866,0.005998458,0.03275752,0.005132262],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6493719,0.00008618589,0.3480098,0.002007571,0.00003166285,0.0002430946,0.000008623417,0.00007693718,0.0001643294],"genre_scores_gemma":[0.9468436,0.00001365739,0.05188087,0.001174254,0.00003022996,0.00003366462,0.00000157478,0.000005942677,0.00001623773],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7615294,"threshold_uncertainty_score":0.5524808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0668372848957785,"score_gpt":0.3114232269928425,"score_spread":0.244585942097064,"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."}}