{"id":"W2073778747","doi":"","title":"Understanding Information Exchange During Disaster Response: Methodological Insights from Infocentric Analysis","year":2014,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Disaster Management and Resilience","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Information exchange; Leverage (statistics); Information mapping; Information economics; Computer science; Information theory; Social exchange theory; Network analysis; Disaster response; Knowledge management; Data science; Information system; Risk analysis (engineering); Business; Emergency management; Economics; Management information systems; Personal information management; Political science; Engineering; Psychology; Microeconomics; Social psychology","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":[0.003654258,0.0001168134,0.0002009892,0.0005061803,0.0008251815,0.0003152288,0.0003710939,0.00009073174,0.0001967863],"category_scores_gemma":[0.0006766025,0.00009217387,0.0001479045,0.001062638,0.0001215098,0.001152184,0.00007305258,0.0005777796,0.00006380832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001210687,"about_ca_system_score_gemma":0.0002575133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000186431,"about_ca_topic_score_gemma":0.002770104,"domain_scores_codex":[0.9965134,0.001261437,0.0002943797,0.0001438948,0.0005465241,0.00124034],"domain_scores_gemma":[0.9991112,0.0003617786,0.0002221498,0.0001310157,0.00005902377,0.000114796],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001745561,0.0001297128,0.02660035,0.00002331903,0.002238644,0.000008818964,0.09420267,0.00199219,0.0003137223,0.8423418,0.0005809497,0.0298222],"study_design_scores_gemma":[0.002389407,0.000378239,0.09379017,0.00004788396,0.001050414,0.000009025249,0.2376745,0.001891068,0.00003409392,0.6197875,0.04208494,0.000862731],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7609109,0.0002537172,0.2214279,0.001081716,0.0002096481,0.000109199,8.253877e-7,0.00004939764,0.01595663],"genre_scores_gemma":[0.996758,0.0005238705,0.0001213972,0.0001445629,0.000242208,0.000002586681,0.000004293978,0.000004509666,0.002198599],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.235847,"threshold_uncertainty_score":0.6346713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06723082318927733,"score_gpt":0.310713190957116,"score_spread":0.2434823677678387,"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."}}