{"id":"W1942491725","doi":"","title":"Manufacturing 'Terrorists': Refugees, National Security and Canadian Law, Part 1","year":2000,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Military and Defense Studies","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Refugee; National security; Political science; Law; Computer security; Business; 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":[0.001850621,0.0005741103,0.0004484708,0.003331024,0.01621228,0.01057545,0.001754548,0.005670177,0.01668738],"category_scores_gemma":[0.006741234,0.0004569878,0.0003543367,0.007963091,0.01160052,0.003382664,0.002337343,0.003648969,0.0005277907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.09356151,"about_ca_system_score_gemma":0.1526462,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9913202,"about_ca_topic_score_gemma":0.9962004,"domain_scores_codex":[0.9972311,0.0003052094,0.00007087535,0.0001725292,0.0008585144,0.001361846],"domain_scores_gemma":[0.9970211,0.001027418,0.0004573486,0.00009146753,0.0008272747,0.0005754291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00004778196,0.0001559241,0.01936863,0.0002982979,0.00002286053,0.0003800704,0.02381839,0.0008403636,0.0002743095,0.7540357,0.1610091,0.03974857],"study_design_scores_gemma":[0.00003538219,0.00008080665,0.1780937,0.001417015,0.000067339,0.000224172,0.0772765,0.001079112,0.0003414988,0.07108971,0.6701549,0.0001398755],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1387544,0.06692939,0.0004613829,0.1615988,0.0008292131,0.0001453985,0.001574727,0.0000287958,0.6296778],"genre_scores_gemma":[0.8268753,0.03483719,0.0004723172,0.01515984,0.0006489441,0.00006348596,0.0004570148,0.00002853315,0.1214573],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.09356151,"threshold_uncertainty_score":0.6788394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008326250068792701,"score_gpt":0.2610386460587092,"score_spread":0.2527123959899165,"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."}}