{"id":"W4387023444","doi":"10.24908/ss.v21i3.16107","title":"Automated Government Benefits and Welfare Surveillance","year":2023,"lang":"en","type":"article","venue":"Surveillance & Society","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Scholarship; Welfare; Accountability; Welfare state; Bureaucracy; Harm; Government (linguistics); Public relations; Political science; Public administration; Law and economics; Sociology; Law; 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":[0.002067757,0.0001732292,0.0002330813,0.00001598718,0.001238285,0.0001530962,0.0003636017,0.0001840928,0.000122695],"category_scores_gemma":[0.0005699007,0.0001785594,0.0001156843,0.0007547343,0.0002625443,0.0002496042,0.0003386337,0.0001786223,0.00008918111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002365169,"about_ca_system_score_gemma":0.00006785232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00201535,"about_ca_topic_score_gemma":0.004816292,"domain_scores_codex":[0.9977537,0.00025348,0.0002184091,0.0004534874,0.0007489008,0.0005719634],"domain_scores_gemma":[0.9990956,0.0001623789,0.0001126439,0.0003737139,0.00009327231,0.0001623679],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005454035,0.000115495,0.6581822,0.0001667438,0.0001487785,0.00001393026,0.02538148,0.00006288702,0.0003443579,0.01141256,0.2567491,0.04736792],"study_design_scores_gemma":[0.000556028,0.00003849057,0.6622279,0.0000190397,0.00000384169,0.000003045686,0.008977459,0.001903362,0.00003112542,0.0005781896,0.3252335,0.0004280144],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.967679,0.00198,0.0000595178,0.01648199,0.001280171,0.0006866754,0.0007606548,0.003279347,0.007792599],"genre_scores_gemma":[0.9923756,0.006548496,0.0001401854,0.0001954987,0.0002847315,0.00003761608,0.00007773968,0.00002069182,0.0003194569],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06848437,"threshold_uncertainty_score":0.9524012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02053191328807975,"score_gpt":0.279421350626133,"score_spread":0.2588894373380533,"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."}}