{"id":"W4239952862","doi":"10.22215/rera.v11i1.257","title":"Making and authenticating the citizen: Naturalisation and passport application in Canada","year":2017,"lang":"en","type":"article","venue":"Review of European and Russian Affairs","topic":"Migration, Refugees, and Integration","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; University of Ottawa","funders":"","keywords":"Naturalisation; Citizenship; Meaning (existential); Context (archaeology); Notice; State (computer science); Public relations; Sociology; Politics; Political science; Interpersonal communication; sort; Internet privacy; Social psychology; Epistemology; Law; Psychology; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.005358292,0.0002884518,0.000470673,0.00226142,0.01220726,0.007211495,0.001900177,0.001162428,0.003578947],"category_scores_gemma":[0.008897047,0.0002048172,0.0003097048,0.006456738,0.007292674,0.002169554,0.002976927,0.001406496,0.0002849069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.09101988,"about_ca_system_score_gemma":0.2291985,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9885855,"about_ca_topic_score_gemma":0.9933429,"domain_scores_codex":[0.9961903,0.0009690095,0.0001654523,0.000350624,0.001486501,0.0008380889],"domain_scores_gemma":[0.9932232,0.001918314,0.0004156196,0.0002236624,0.003359788,0.0008594707],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002755772,0.0001756975,0.03421588,0.004141473,0.0000770525,0.004278359,0.2133687,0.001261564,0.001308931,0.1253997,0.03154765,0.5839494],"study_design_scores_gemma":[0.00002401791,0.0000955404,0.07158604,0.003774397,0.0001127073,0.0006678373,0.1818563,0.0007222592,0.001485633,0.004904326,0.7346124,0.0001585693],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5105473,0.159376,0.004012693,0.0365089,0.0007811364,0.0004816333,0.0008160805,0.0001851405,0.287291],"genre_scores_gemma":[0.9161046,0.06025257,0.002684557,0.001242838,0.00002715828,0.00006604924,0.0002397589,0.0000407227,0.01934179],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09101988,"threshold_uncertainty_score":0.6603986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02110772384953175,"score_gpt":0.2991669950848801,"score_spread":0.2780592712353483,"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."}}