{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002261292,0.0002976226,0.0002448322,0.001074838,0.007840213,0.01209497,0.0008890649,0.004203808,0.02070044],"category_scores_gemma":[0.01729551,0.0002975105,0.0002622951,0.002927202,0.009702738,0.01314584,0.005302692,0.007308656,0.003852945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005916261,"about_ca_system_score_gemma":0.008095755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08434984,"about_ca_topic_score_gemma":0.1374952,"domain_scores_codex":[0.9977648,0.0004747393,0.000118721,0.0001665289,0.0007942423,0.0006809078],"domain_scores_gemma":[0.9912554,0.002991752,0.0007487687,0.001098013,0.002513705,0.00139226],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00004036942,0.00001219042,0.001771529,0.00005386547,0.000006834362,0.0002367428,0.007214382,0.00008029016,0.00009554008,0.4943479,0.4718373,0.02430298],"study_design_scores_gemma":[0.000006195365,0.00001189816,0.003652332,0.0001839591,0.0000107833,0.0001888116,0.007271205,0.0001135578,0.0002510252,0.03590551,0.9523736,0.00003101608],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01809578,0.01476663,0.001926585,0.5779138,0.008415678,0.00002019874,0.0009307408,0.0003921363,0.3775384],"genre_scores_gemma":[0.6268232,0.01078293,0.001612927,0.1055003,0.003525325,0.00006057252,0.0006520288,0.000541618,0.2505012],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08434984,"threshold_uncertainty_score":0.1677178,"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."}}