{"id":"W2947279644","doi":"","title":"The Process and Implications of Racialization: A Case Study","year":2004,"lang":"en","type":"dissertation","venue":"TSpace","topic":"Migration, Ethnicity, and Economy","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Racialization; Process (computing); Sociology; Genealogy; Gender studies; Race (biology); History; Computer science","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":[0.008003444,0.001145708,0.000735983,0.002150218,0.04559529,0.007350275,0.003266845,0.006265222,0.00330673],"category_scores_gemma":[0.01067835,0.0008609121,0.0008122223,0.003512064,0.01813275,0.006100649,0.01108852,0.008652677,0.0004154769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01130233,"about_ca_system_score_gemma":0.007369609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0545551,"about_ca_topic_score_gemma":0.1042722,"domain_scores_codex":[0.9894267,0.007669299,0.0001956038,0.0004152817,0.0006753152,0.001617858],"domain_scores_gemma":[0.9943354,0.003600921,0.0005940655,0.0002850993,0.0003375873,0.0008469155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00003199036,0.0002241075,0.002869661,0.00009046494,0.000005917452,0.02396942,0.9358906,0.0001007352,0.0003292779,0.02770327,0.002152084,0.006632515],"study_design_scores_gemma":[0.000006470145,0.00005732414,0.001246195,0.0002252395,0.000009964615,0.00823943,0.9552536,0.0001796645,0.0003302337,0.002193404,0.03223866,0.00001983192],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9149107,0.003969751,0.003719842,0.01611555,0.0004053011,0.0003787063,0.00007366686,0.00004011677,0.06038626],"genre_scores_gemma":[0.9783462,0.004492246,0.00258241,0.002075364,0.0001218299,0.0002413282,0.00003020779,0.00004024489,0.01207021],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0545551,"threshold_uncertainty_score":0.1084751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03019918648999937,"score_gpt":0.4159210249617175,"score_spread":0.3857218384717181,"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."}}