{"id":"W4291237065","doi":"","title":"2016\"Elderly and IT: Brand Discourses on the Go\"Human Aspects of IT for the Aged Population. Healthy and Active Aging, Second International Conference, ITAP 2016, Held as Part of HCI International 2016 Toronto, ON, Canada, July 17–22, 2016, Proceedings, Part II, Jia Zhou, Gavriel Salvendy Dir., Springer, pp.186-193.","year":2016,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Technology Use by Older Adults","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Population ageing; Gerontology; Computer science; Media studies; Population; Sociology; Medicine; Demography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002540873,0.0002875066,0.0002277246,0.0005979477,0.002386853,0.004620926,0.0002224725,0.001316151,0.006218228],"category_scores_gemma":[0.004052427,0.0001311478,0.0001219651,0.00114338,0.00285521,0.005518693,0.002655112,0.001349195,0.0005866915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00175738,"about_ca_system_score_gemma":0.001607408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01062376,"about_ca_topic_score_gemma":0.01533343,"domain_scores_codex":[0.9994996,0.0002687894,0.00003510052,0.00005280954,0.0001004615,0.00004319464],"domain_scores_gemma":[0.9987901,0.0005514182,0.0002013855,0.00006584257,0.0001813509,0.0002100085],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002883994,0.00004981994,0.01285471,0.0006245762,0.00002268489,0.0004152817,0.6253854,0.0001024429,0.001589666,0.1152241,0.1211983,0.1222446],"study_design_scores_gemma":[0.00002585819,0.00007445999,0.03572154,0.001314405,0.00005472843,0.000322424,0.3340794,0.0002621974,0.0009510739,0.02408274,0.6030633,0.00004786427],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6167344,0.04363905,0.003069209,0.1250974,0.004225756,0.00004124824,0.0008224284,0.0001076912,0.2062628],"genre_scores_gemma":[0.9621642,0.009733961,0.0003908756,0.003930113,0.0006521802,0.00002165529,0.0001489657,0.00005085211,0.02290718],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01062376,"threshold_uncertainty_score":0.02112383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02016490918047749,"score_gpt":0.2842310330907888,"score_spread":0.2640661239103113,"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."}}