{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.009679057,0.0003186229,0.0003111428,0.002777719,0.004663208,0.01009009,0.00101589,0.004235839,0.005960467],"category_scores_gemma":[0.02898702,0.0003086239,0.0004719491,0.002107594,0.01515285,0.007735417,0.006393953,0.00325336,0.0006781896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005525472,"about_ca_system_score_gemma":0.005894851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006301396,"about_ca_topic_score_gemma":0.004650342,"domain_scores_codex":[0.9876608,0.006693811,0.0003877553,0.001088548,0.002761556,0.00140763],"domain_scores_gemma":[0.9797967,0.01087693,0.003850114,0.002701996,0.002144681,0.0006295088],"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":[0.00002074569,0.00003650307,0.005028072,0.00003796797,0.000008596861,0.0001019586,0.002528563,0.0008974404,0.0001201056,0.9578068,0.003543704,0.02986952],"study_design_scores_gemma":[0.00002715966,0.00005831345,0.01073475,0.0004218713,0.00002653392,0.000237875,0.004387422,0.004320141,0.0007136143,0.8572938,0.1217266,0.0000518302],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1879641,0.004128344,0.07275718,0.1121051,0.0005698684,0.0001911992,0.0002611308,0.0002305249,0.6217924],"genre_scores_gemma":[0.983184,0.001177069,0.003976163,0.002689538,0.0002733964,0.00007500126,0.00005656928,0.00002250598,0.008545614],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9953368,"threshold_uncertainty_score":0.05118841,"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."}}