{"id":"W2958488582","doi":"","title":"In Code We Trust! India's Demonetization, Trust Ambivalence & Electronic Currencies.","year":2019,"lang":"en","type":"article","venue":"Americas Conference on Information Systems","topic":"Indian Economic and Social Development","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Ambivalence; Code (set theory); Computer security; Computer science; Internet privacy; Electronic money; Business; Commerce; World Wide Web; Social psychology; Psychology; Programming language","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005451791,0.0002034595,0.0005147546,0.0004480385,0.00008837417,0.0001993948,0.0003110732,0.0001417135,0.0005843304],"category_scores_gemma":[0.00005044679,0.0002416878,0.00006192015,0.0004329638,0.00006049065,0.001371958,0.00003382199,0.0002514343,0.01193466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004828607,"about_ca_system_score_gemma":0.0002222688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003817393,"about_ca_topic_score_gemma":0.000009901508,"domain_scores_codex":[0.9979628,0.00003003046,0.001203128,0.0002656867,0.00008677081,0.0004516002],"domain_scores_gemma":[0.9986511,0.00003491376,0.0008680641,0.0002891558,0.00007433031,0.00008240099],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001555354,0.00002634272,0.07535522,0.00006093022,0.0000241844,5.344659e-7,0.005163096,0.0005053867,0.000001260747,0.916127,0.0009287344,0.001791727],"study_design_scores_gemma":[0.002784034,0.0004241919,0.1410547,0.0002781269,0.000007424945,0.00001433477,0.01242279,0.05991337,0.00004116081,0.01359074,0.7677798,0.001689235],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4992588,0.0008505601,0.008360456,0.00147882,0.003358665,0.0020607,0.000316968,0.0001452126,0.4841698],"genre_scores_gemma":[0.9959332,0.0008812987,0.00004531042,0.0007091993,0.0000438303,0.0001078565,0.00006155157,0.0000126662,0.002205139],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9025363,"threshold_uncertainty_score":0.9888347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02281948141610783,"score_gpt":0.2258694482871315,"score_spread":0.2030499668710236,"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."}}