{"id":"W2990843083","doi":"10.1109/mahc.2019.2896282","title":"The Development of Consent to Computing","year":2019,"lang":"en","type":"article","venue":"IEEE Annals of the History of Computing","topic":"History of Computing Technologies","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Negotiation; Telematics; History of computing; Transparency (behavior); Politics; Digital transformation; Work (physics); Political science; Computer science; Law; Telecommunications; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.02154439,0.0005136894,0.0006243751,0.002737611,0.006834129,0.01400429,0.001561625,0.006072843,0.005880495],"category_scores_gemma":[0.03704802,0.0005780953,0.000567439,0.00318365,0.08735674,0.01939426,0.008228047,0.009418629,0.001102153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01305011,"about_ca_system_score_gemma":0.01269692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006462666,"about_ca_topic_score_gemma":0.003141259,"domain_scores_codex":[0.9744066,0.0160625,0.001138055,0.002771484,0.003946037,0.001675358],"domain_scores_gemma":[0.9732001,0.01797277,0.00102579,0.004275955,0.002709003,0.0008163873],"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":[0.000003218961,0.000002203095,0.0000518567,0.0000150845,6.841078e-7,0.00002422549,0.002321243,0.00006165833,0.00002241438,0.9931306,0.001065793,0.003300969],"study_design_scores_gemma":[0.00001080882,0.00001745865,0.000199795,0.0003155016,0.000003206494,0.0001247171,0.00214456,0.0003288109,0.0002762861,0.7612753,0.2352826,0.0000210302],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.02290357,0.01635826,0.05533068,0.1054565,0.002154056,0.0002002518,0.0001662855,0.0001417238,0.7972886],"genre_scores_gemma":[0.9010822,0.01146088,0.01137652,0.01624186,0.001975991,0.0003645024,0.0001591641,0.0002505554,0.0570885],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9931659,"threshold_uncertainty_score":0.113939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09183375711251468,"score_gpt":0.2960366687367714,"score_spread":0.2042029116242567,"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."}}