{"id":"W3124297635","doi":"10.1177/0095399712469198","title":"Understanding Information Exchange During Disaster Response","year":2012,"lang":"en","type":"article","venue":"Administration & Society","topic":"Disaster Management and Resilience","field":"Social Sciences","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Information exchange; Leverage (statistics); Information mapping; Computer science; Information theory; Information economics; Social exchange theory; Information system; Information needs; Knowledge management; Data science; Risk analysis (engineering); Management science; Business; Personal information management; Management information systems; Sociology; Economics; Political science; Microeconomics; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0008355011,0.00006965124,0.00005425356,0.00002277024,0.000647825,0.0001862345,0.0001045284,0.00006405093,0.0002525473],"category_scores_gemma":[0.00006008589,0.00006846937,0.00007028697,0.000188519,0.0001441792,0.002426126,0.00003218423,0.000059789,0.0001058184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002137021,"about_ca_system_score_gemma":0.00006213914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001475839,"about_ca_topic_score_gemma":0.00004529249,"domain_scores_codex":[0.9990484,0.000108466,0.0001488355,0.00007264611,0.000328555,0.0002931154],"domain_scores_gemma":[0.9996314,0.00005851749,0.00008166992,0.00009584959,0.0000232615,0.0001092984],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.000320725,0.0001747313,0.02144813,0.0001313044,0.00005666147,0.000001174675,0.8616388,0.00001195973,0.0009346909,0.0833966,0.03081562,0.001069605],"study_design_scores_gemma":[0.0008309833,0.00009500826,0.114832,0.00004513783,0.00004383962,0.000002196667,0.6232306,0.00008096499,0.0004693295,0.0008913369,0.2589714,0.0005072039],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9384307,0.0000354204,0.003997385,0.003012584,0.0004777597,0.0002625419,0.000003819797,0.0001199835,0.05365979],"genre_scores_gemma":[0.9964846,0.00002922819,0.000204472,0.0002760264,0.0002146533,0.00001268724,0.00000881712,0.000003793218,0.002765683],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2384082,"threshold_uncertainty_score":0.4982612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08803863592567444,"score_gpt":0.3195230821170404,"score_spread":0.231484446191366,"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."}}