{"id":"W2891994155","doi":"10.23889/ijpds.v3i4.987","title":"Empathic Cultural Mapping: Little data, big data, knowledge transfer and exchange","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Government (linguistics); Public relations; Empowerment; Psychosocial; Sociology; Big data; Data science; Political science; Psychology; Computer science","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":[0.03382539,0.0006141467,0.0005907008,0.004174855,0.009939862,0.02181673,0.003095388,0.002121907,0.01208042],"category_scores_gemma":[0.07733139,0.0005252373,0.0004910446,0.008386094,0.01849428,0.02231698,0.02054772,0.003656978,0.001232372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007308678,"about_ca_system_score_gemma":0.008518565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009901756,"about_ca_topic_score_gemma":0.008976622,"domain_scores_codex":[0.961172,0.03067077,0.0008484913,0.002351341,0.003700075,0.001257362],"domain_scores_gemma":[0.9130812,0.06021219,0.00458656,0.01316592,0.005469228,0.003484904],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0001277061,0.000220866,0.02531374,0.00163842,0.00009625719,0.0009612574,0.432797,0.001350487,0.0003975177,0.1941277,0.0389662,0.3040028],"study_design_scores_gemma":[0.00001929223,0.00007257608,0.01136204,0.002547419,0.00003147168,0.0005624552,0.4969091,0.002984136,0.0006265283,0.2255278,0.2592804,0.00007674802],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3009928,0.01122664,0.1377743,0.1678755,0.001583199,0.00141824,0.003402646,0.0009631615,0.3747635],"genre_scores_gemma":[0.9381006,0.004594713,0.0401776,0.004083666,0.0002637235,0.0009867884,0.001141619,0.0002404138,0.01041093],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03382539,"threshold_uncertainty_score":0.178888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2582180060156925,"score_gpt":0.4432382665093945,"score_spread":0.185020260493702,"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."}}