{"id":"W2954844427","doi":"10.21307/connections-2019-003","title":"What the eye does not see: visualizations strategies for the data collection of personal networks","year":2019,"lang":"en","type":"article","venue":"Connections","topic":"Community Health and Development","field":"Health Professions","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Visualization; Computer science; Data collection; Representation (politics); Process (computing); Data science; Key (lock); Personal network; Relational database; Data visualization; Human–computer interaction; Information retrieval; Artificial intelligence; Sociology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03046633,0.001581163,0.000868619,0.008640779,0.003574798,0.01084461,0.002278545,0.001695663,0.007123866],"category_scores_gemma":[0.1223624,0.001010391,0.0007961516,0.008013104,0.004021617,0.009530094,0.007342482,0.002004434,0.001499354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001989483,"about_ca_system_score_gemma":0.002490137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002076303,"about_ca_topic_score_gemma":0.002744691,"domain_scores_codex":[0.9623017,0.03262307,0.001402359,0.00101598,0.002237927,0.0004188475],"domain_scores_gemma":[0.8495925,0.125293,0.003851171,0.01167559,0.008121794,0.001465962],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0006605479,0.0002032953,0.006444079,0.002460556,0.0001095743,0.00130421,0.4850577,0.003296855,0.008125527,0.1269314,0.03494142,0.3304648],"study_design_scores_gemma":[0.0002924791,0.0004041861,0.00838962,0.006230137,0.000208638,0.001513495,0.21965,0.04888177,0.01462947,0.2298282,0.46952,0.0004520075],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07400388,0.001557462,0.8746796,0.008291956,0.0004516318,0.002411677,0.001998418,0.008227348,0.02837797],"genre_scores_gemma":[0.2873519,0.001077143,0.7007958,0.0003793451,0.0001334687,0.003820998,0.0007726442,0.001333813,0.004334922],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03046633,"threshold_uncertainty_score":0.1611233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1439619025823389,"score_gpt":0.4664317236045632,"score_spread":0.3224698210222243,"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."}}