{"id":"W2158157888","doi":"10.3917/rdli.083.0051","title":"Les statistiques « ethniques » outillent des politiques de quotas plutôt que la connaissance des discriminations : l’exemple canadien","year":2015,"lang":"fr","type":"article","venue":"La Revue de l Ires","topic":"Canadian Identity and History","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Art","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":[0.005041947,0.0004958097,0.0006198863,0.003728912,0.006355474,0.007540173,0.0008592415,0.001719338,0.01075955],"category_scores_gemma":[0.0106187,0.0002481974,0.0005220009,0.006819074,0.01292145,0.005654023,0.002874655,0.003140006,0.0007269148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01260211,"about_ca_system_score_gemma":0.007512447,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1022258,"about_ca_topic_score_gemma":0.1379813,"domain_scores_codex":[0.9930675,0.003600341,0.0001744645,0.001001888,0.001414676,0.0007410595],"domain_scores_gemma":[0.9903082,0.006292482,0.000898018,0.0009379531,0.001280001,0.0002832766],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0000459123,0.00001975754,0.005896139,0.0002881058,0.00005544602,0.0001385293,0.03158404,0.0003516717,0.0002778383,0.9234473,0.004419558,0.03347592],"study_design_scores_gemma":[0.00002517588,0.00007536241,0.04700656,0.00142265,0.00009429271,0.0002945872,0.03803148,0.00071606,0.0009692672,0.2562229,0.6550443,0.00009724147],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1900278,0.03902726,0.01678388,0.06435592,0.0009327648,0.0000843677,0.0008658107,0.0001002052,0.687822],"genre_scores_gemma":[0.939813,0.008871546,0.002910211,0.003630117,0.000333562,0.00009941996,0.0002310411,0.00008287406,0.04402832],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8977742,"threshold_uncertainty_score":0.2032616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09848735873907029,"score_gpt":0.3496425476614746,"score_spread":0.2511551889224043,"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."}}