{"id":"W2725254759","doi":"10.31235/osf.io/6cdhe","title":"Logics and practices of transparency and opacity in real-world applications of public sector machine learning","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Regulation and Compliance Studies","field":"Business, Management and Accounting","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Transparency (behavior); Accountability; Public sector; Public relations; Nova scotia; Business; Political science; Computer science; Sociology; Computer security","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003827011,0.0001309125,0.0003233457,0.0002466363,0.0001225775,0.0001462594,0.0001626221,0.00005631227,0.00006214283],"category_scores_gemma":[0.0001297342,0.0001126086,0.00003095772,0.0001348657,0.0001339174,0.0003774565,0.0003256224,0.0002029185,8.351548e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006827696,"about_ca_system_score_gemma":0.00001609067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005498487,"about_ca_topic_score_gemma":0.01066233,"domain_scores_codex":[0.9992127,0.00001541933,0.0003044796,0.0002519689,0.0001203628,0.00009499952],"domain_scores_gemma":[0.9983473,0.00005539994,0.001217159,0.0002225411,0.0001504941,0.000007057724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000009996853,0.00005399447,0.9585152,0.001319322,0.00003300515,2.52324e-7,0.00008322346,0.00006589101,0.00003427517,0.02812064,0.00003768847,0.01172651],"study_design_scores_gemma":[0.0002746961,0.00000481131,0.9690005,0.0001233433,0.00004907664,2.123422e-7,0.0002184224,0.005897646,0.000009333075,0.01254826,0.01170555,0.0001681391],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6644343,0.002292722,0.005738534,0.004745222,0.0001403113,0.001295585,0.00002500535,0.0001161332,0.3212122],"genre_scores_gemma":[0.9974929,0.001170319,0.0004478683,0.00002182546,0.00008624375,0.00004274866,0.0000324365,0.000007436787,0.0006982334],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3330586,"threshold_uncertainty_score":0.8312104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1574278819720679,"score_gpt":0.3451709249132124,"score_spread":0.1877430429411445,"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."}}