{"id":"W4315645158","doi":"10.2139/ssrn.4319976","title":"Admissibility of Hearsay Gathered Under MLAT: A Tempest in Canada","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"European Criminal Justice and Data Protection","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Hearsay; Tempest; Political science; Law; Archaeology; History; Geology; Ancient history; Art history","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":[0.01018866,0.0002763159,0.0007296285,0.002490548,0.01907749,0.007626943,0.00432826,0.004300948,0.007322962],"category_scores_gemma":[0.05557965,0.0004940752,0.0005097361,0.002566214,0.005775683,0.001987185,0.003275745,0.006097755,0.0009862492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.07006995,"about_ca_system_score_gemma":0.1430214,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9933859,"about_ca_topic_score_gemma":0.9958639,"domain_scores_codex":[0.9850057,0.001552284,0.000505136,0.001333951,0.007967374,0.003635502],"domain_scores_gemma":[0.9419737,0.01212469,0.001612923,0.002427115,0.03638265,0.005478812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002309943,0.0007254818,0.383583,0.0004235493,0.0003505418,0.008804138,0.1640062,0.004559189,0.003892094,0.1028827,0.1390939,0.1893693],"study_design_scores_gemma":[0.0002382946,0.0006489842,0.4081835,0.0009457142,0.0006222301,0.001788039,0.2319401,0.01243587,0.006692714,0.01614879,0.3197307,0.0006249433],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8733819,0.001178743,0.001212224,0.03296395,0.0004435601,0.0001756785,0.001728637,0.0001348491,0.08878064],"genre_scores_gemma":[0.9637458,0.0003375764,0.0005960811,0.002742372,0.00007431137,0.00001733317,0.0003297728,0.00005253874,0.03210417],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07006995,"threshold_uncertainty_score":0.5083954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0279234033224772,"score_gpt":0.2959911396208387,"score_spread":0.2680677362983614,"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."}}