{"id":"W4387761676","doi":"10.51952/9781447320081.ch002","title":"Policy and biopolitics: the event of race-based statistics in Toronto","year":2017,"lang":"en","type":"book-chapter","venue":"Policy Press eBooks","topic":"Census and Population Estimation","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Biopower; Race (biology); Event (particle physics); Statistics; Geography; History; Sociology; Political science; Gender studies; Mathematics; Law; Politics; Physics","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.004303043,0.0004892717,0.0004505129,0.001501046,0.00494932,0.006089444,0.000970374,0.003134405,0.01814446],"category_scores_gemma":[0.01582014,0.0005723417,0.0002974578,0.00482073,0.009765304,0.003796774,0.002335355,0.004559069,0.00135075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.07293341,"about_ca_system_score_gemma":0.05682127,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9134082,"about_ca_topic_score_gemma":0.9451784,"domain_scores_codex":[0.9965544,0.001258123,0.0001379355,0.000251822,0.001174686,0.0006230448],"domain_scores_gemma":[0.9929912,0.0036947,0.000331099,0.0005291734,0.001407041,0.001046826],"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":[0.00001862991,0.000005853973,0.001148016,0.0000865797,0.000007333332,0.00008868362,0.002871832,0.0007785481,0.00007907455,0.6036336,0.3569655,0.0343164],"study_design_scores_gemma":[0.000006373554,0.00001055323,0.01028802,0.0004017355,0.00001184169,0.00003355767,0.00326114,0.001479409,0.0001922619,0.1084989,0.8757721,0.00004416089],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01066733,0.05494967,0.006856614,0.5493082,0.004681056,0.00004552491,0.002296356,0.0001905012,0.3710048],"genre_scores_gemma":[0.3921738,0.0699321,0.005747969,0.02762295,0.005068418,0.0001460871,0.00133922,0.0004550418,0.4975143],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9950507,"threshold_uncertainty_score":0.5291713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08417518591728439,"score_gpt":0.3880292151594644,"score_spread":0.30385402924218,"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."}}