{"id":"W3042740169","doi":"10.33137/ijidi.v4i2.34569","title":"Forced Migration","year":2020,"lang":"en","type":"article","venue":"The International Journal of Information Diversity & Inclusion (IJIDI)","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Forced migration; Materials science; Political science; Law; Refugee","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004135808,0.001353459,0.001135453,0.002379291,0.002491962,0.005960659,0.001842344,0.007306187,0.034664],"category_scores_gemma":[0.03016042,0.0005319047,0.001390245,0.0009866467,0.001892435,0.001951008,0.001815398,0.009158123,0.01152667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001785115,"about_ca_system_score_gemma":0.002924964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001498362,"about_ca_topic_score_gemma":0.003412318,"domain_scores_codex":[0.9969193,0.0005447576,0.0003512583,0.0005561397,0.001200137,0.0004283553],"domain_scores_gemma":[0.9767602,0.007940462,0.001681571,0.001343462,0.008715674,0.003558644],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00001359555,0.000006425402,0.0000754636,0.00009817738,0.000007582303,0.0001030227,0.00001894319,0.000006207742,0.0000208628,0.0002353074,0.9957456,0.003668768],"study_design_scores_gemma":[0.00003272842,0.00001632523,0.0006639474,0.0004205737,0.00002355546,0.000224362,0.0001184305,0.00004279955,0.00007778022,0.0004580514,0.9979116,0.000009781095],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"editorial","genre_gemma":"empirical","genre_scores_codex":[0.0001199301,0.001823141,0.0000391718,0.03400972,0.961224,0.00001316025,0.0000823638,0.00002431129,0.002664059],"genre_scores_gemma":[0.002293726,0.002694393,0.00008045215,0.03035731,0.9477729,0.00003204297,0.00008076364,0.00004086284,0.01664759],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.034664,"threshold_uncertainty_score":0.1159626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01828032051634607,"score_gpt":0.2661517251093751,"score_spread":0.2478714045930291,"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."}}