{"id":"W7024159160","doi":"","title":"Questions of Recognition? Critical Investigations of Citizenship and Culture in Multicultural Canadian Writing","year":2019,"lang":"en","type":"article","venue":"OPUS (Augsburg University)","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multiculturalism; Citizenship; Immigration; Cultural diversity","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01492774,0.000685808,0.0006639991,0.005873978,0.04036794,0.01763654,0.002406208,0.002159534,0.004311211],"category_scores_gemma":[0.03610406,0.0003955312,0.000268413,0.008780151,0.04565876,0.006496694,0.008333958,0.003731447,0.0001860023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.08254027,"about_ca_system_score_gemma":0.06158459,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8230319,"about_ca_topic_score_gemma":0.8448705,"domain_scores_codex":[0.9894811,0.005257069,0.000546895,0.0009577431,0.001890659,0.001866541],"domain_scores_gemma":[0.9615185,0.02456937,0.00203154,0.001908897,0.008129789,0.001841845],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00001267228,0.000006412783,0.0008246672,0.00004420598,0.000001833465,0.0001307082,0.9769459,0.00001279269,0.00008476405,0.0173,0.001024213,0.003611798],"study_design_scores_gemma":[0.000002309107,0.000004275186,0.002022498,0.0001025862,0.000004034762,0.0000536196,0.9697838,0.00004041649,0.0001355285,0.002266043,0.02557136,0.00001347808],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7870887,0.007368014,0.001421342,0.02646867,0.0007295346,0.000211828,0.0001812713,0.00004366892,0.1764869],"genre_scores_gemma":[0.9932215,0.0008060172,0.0002984389,0.0007755799,0.00005545836,0.00004298502,0.00002704591,0.00001912272,0.004753869],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1769681,"threshold_uncertainty_score":0.5988744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02545522935389424,"score_gpt":0.2402605438046537,"score_spread":0.2148053144507594,"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."}}