{"id":"W4414015804","doi":"10.11159/mhci25.121","title":"Beyond “Alexa, good morning”: prerequisites for a voice assistant that truly understands older adults with empathy","year":2025,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"AI in Service Interactions","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundação para a Ciência e a Tecnologia","keywords":"Empathy; Computer science; Morning; Human–computer interaction; Speech recognition; Psychology; Medicine; Social psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003441536,0.0002049547,0.0002718832,0.0003603776,0.0003226841,0.0006312386,0.000922455,0.00003963006,1.54865e-7],"category_scores_gemma":[0.00003350088,0.0001361163,0.00004543381,0.001445668,0.0001253905,0.0005076054,0.0002575418,0.0002061694,1.545106e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007348667,"about_ca_system_score_gemma":0.00006501595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001215546,"about_ca_topic_score_gemma":0.000003953763,"domain_scores_codex":[0.99845,0.000005647998,0.0002404,0.0005547443,0.0003824541,0.0003667273],"domain_scores_gemma":[0.9989451,0.0002849139,0.0001608425,0.000200461,0.0003028151,0.0001058532],"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":[0.000175928,0.0002159834,0.007868026,0.001324666,0.0001622091,0.000002760399,0.002177614,0.003724628,0.001698203,0.9700221,0.001680697,0.01094718],"study_design_scores_gemma":[0.000597537,0.0002810829,0.00572429,0.001415536,0.00002125462,0.00003315175,0.00008406761,0.9882043,0.002512781,0.0002410987,0.000664309,0.0002205728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7316968,0.001641823,0.2560061,0.002276968,0.003180975,0.001839277,0.000008075725,0.0004111299,0.002938788],"genre_scores_gemma":[0.9948028,0.000007665131,0.004359413,0.0001262037,0.00005307682,0.00006063803,1.122801e-7,0.000008421361,0.0005816814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9844797,"threshold_uncertainty_score":0.6087048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006024139139036895,"score_gpt":0.2128292833368796,"score_spread":0.2068051441978427,"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."}}