{"id":"W2968503355","doi":"10.22148/16.045","title":"Performative Data: Cultures of Government Data Practice","year":2019,"lang":"en","type":"article","venue":"Journal of Cultural Analytics","topic":"E-Government and Public Services","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Performative utterance; Government (linguistics); Computer science; Art; Aesthetics; Linguistics; Philosophy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.06392511,0.000856096,0.0008129826,0.005953224,0.01870361,0.040672,0.003237385,0.004912326,0.003409911],"category_scores_gemma":[0.0726764,0.0007703053,0.0007115202,0.009569961,0.1124541,0.02693902,0.02448888,0.0108071,0.0009887867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01758149,"about_ca_system_score_gemma":0.01774181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01379468,"about_ca_topic_score_gemma":0.006215154,"domain_scores_codex":[0.8759465,0.0939166,0.00440383,0.005912728,0.01562976,0.004190646],"domain_scores_gemma":[0.9122856,0.05166608,0.005964746,0.01593347,0.009139033,0.005011105],"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":[0.00001536043,0.00002401936,0.001811583,0.0000811061,0.00001625135,0.0001535253,0.4065716,0.0002015581,0.0001685308,0.5735258,0.004380101,0.0130505],"study_design_scores_gemma":[0.00001639089,0.00003187359,0.001157234,0.0006554163,0.00001692455,0.0002810399,0.3989359,0.0008428532,0.0005969,0.284449,0.3129383,0.00007815426],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2053785,0.006542641,0.09777126,0.1444101,0.001313037,0.0002805145,0.0003506582,0.0004854225,0.5434679],"genre_scores_gemma":[0.9790015,0.001662644,0.00786643,0.003762755,0.0001820485,0.0001918598,0.0001036417,0.000205044,0.007023978],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9812964,"threshold_uncertainty_score":0.3380723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07885178304177048,"score_gpt":0.3910527871218056,"score_spread":0.3122010040800351,"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."}}