{"id":"W2994809930","doi":"10.12927/hcq.2019.26020","title":"Case Study: Innovation Procurement for a “Smart” Privacy Solution","year":2019,"lang":"en","type":"article","venue":"Healthcare Quarterly","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Procurement; Business; Test (biology); Competitive advantage; Patient privacy; Computer security; Phase (matter); Internet privacy; Computer science; Marketing; Health care; Law; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008090554,0.0005675644,0.0003358752,0.00139393,0.01264627,0.005351027,0.002604805,0.007593066,0.008371321],"category_scores_gemma":[0.01161473,0.0004399497,0.0008694117,0.001491703,0.005675788,0.004482657,0.005228021,0.003592542,0.001420174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007683869,"about_ca_system_score_gemma":0.008902414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01134998,"about_ca_topic_score_gemma":0.01833217,"domain_scores_codex":[0.98475,0.009039917,0.0003943363,0.000774937,0.002927361,0.002113392],"domain_scores_gemma":[0.988356,0.006527349,0.0007902704,0.0009277079,0.00146009,0.001938676],"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":[0.0005535592,0.00228018,0.0256155,0.001268631,0.00008227649,0.07786175,0.1937886,0.0113832,0.01041912,0.5550064,0.0388202,0.08292067],"study_design_scores_gemma":[0.0001544374,0.001240359,0.009863631,0.001117664,0.00006939946,0.03466291,0.3424318,0.021198,0.01503169,0.0387001,0.535322,0.0002080816],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6297014,0.001035347,0.08798477,0.02938774,0.0003240684,0.00202402,0.0003504155,0.0002802478,0.2489119],"genre_scores_gemma":[0.9300813,0.0005174433,0.03714245,0.001733406,0.00008169655,0.0005602895,0.0001284064,0.00006212793,0.02969279],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01264627,"threshold_uncertainty_score":0.05575067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1241067751591494,"score_gpt":0.4392240175124151,"score_spread":0.3151172423532657,"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."}}