{"id":"W4382355891","doi":"10.1177/20539517231171053","title":"Prediction as extraction of discretion","year":2023,"lang":"en","type":"article","venue":"Big Data & Society","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Discretion; Officer; Predictive power; Productivity; Redistribution (election); Power (physics); Focus (optics); Work (physics); Sociology; Computer science; Economics; Law; Political science; Epistemology; Engineering; Politics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002041459,0.0000541001,0.00008552229,0.00001827167,0.000542943,0.00007636646,0.0003023006,0.0001915644,0.00006136184],"category_scores_gemma":[0.0006777297,0.00005487517,0.00007445949,0.0004517568,0.000258366,0.0007493793,0.0000876339,0.0001677088,0.00006367036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006082062,"about_ca_system_score_gemma":0.000246062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006299213,"about_ca_topic_score_gemma":0.001209229,"domain_scores_codex":[0.9988822,0.00009458661,0.0001498505,0.0001848297,0.0004947431,0.0001937356],"domain_scores_gemma":[0.9992352,0.0001522817,0.0000994367,0.0003018152,0.000135712,0.00007553006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003599929,0.0002668097,0.0048728,0.0001251932,0.0002200467,0.000004026953,0.2076681,0.00003130332,0.01202567,0.04551745,0.5847131,0.1445195],"study_design_scores_gemma":[0.0005465061,0.0001260023,0.05762611,0.0001015849,0.0001058088,8.83514e-7,0.1363939,0.0019707,0.0005087304,0.03704388,0.76523,0.0003459164],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8452883,0.0003833925,0.006824624,0.049166,0.007317178,0.001031954,0.00250212,0.001280047,0.08620641],"genre_scores_gemma":[0.9921743,0.004209596,0.0001861171,0.0001973971,0.0008820998,0.000003791135,0.0006120828,0.000008987341,0.00172558],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1805169,"threshold_uncertainty_score":0.9522567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3282374062916354,"score_gpt":0.445609573196359,"score_spread":0.1173721669047236,"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."}}