{"id":"W4392736454","doi":"10.48550/arxiv.2403.05573","title":"Beyond Predictive Algorithms in Child Welfare","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gender, Labor, and Family Dynamics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Welfare; Computer science; Algorithm; Economics; Artificial intelligence; Machine learning; Market economy","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.02176596,0.00171109,0.001557827,0.004725112,0.00125296,0.005420452,0.002693013,0.002179665,0.002996384],"category_scores_gemma":[0.08737882,0.0007588555,0.001191066,0.004102326,0.002833746,0.005546902,0.003164514,0.00507625,0.0006088837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00315974,"about_ca_system_score_gemma":0.003030881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.012668,"about_ca_topic_score_gemma":0.009659929,"domain_scores_codex":[0.9892903,0.007348184,0.0004915751,0.001432684,0.001104109,0.0003331685],"domain_scores_gemma":[0.8923619,0.09601379,0.003676512,0.003612499,0.003603122,0.0007321861],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002518908,0.0003571289,0.04057046,0.0005155372,0.0004503311,0.0002083126,0.001856672,0.6476314,0.0003374272,0.07907882,0.004653235,0.2240888],"study_design_scores_gemma":[0.00002445534,0.00004453366,0.001725867,0.000171176,0.00002990145,0.00002929493,0.0003169577,0.8405784,0.0002899705,0.154686,0.002077539,0.00002578832],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09168714,0.003241159,0.8868883,0.007678225,0.0002070386,0.0004633429,0.0007683004,0.001177656,0.0078888],"genre_scores_gemma":[0.7443594,0.001337137,0.2486657,0.001274466,0.0003908532,0.0006012736,0.001425734,0.0001413217,0.001804092],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9987471,"threshold_uncertainty_score":0.1151108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03384062802075889,"score_gpt":0.2056683634111221,"score_spread":0.1718277353903632,"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."}}