{"id":"W4226152167","doi":"10.1287/isre.2021.1077","title":"Identifying Perverse Incentives in Buyer Profiling on Online Trading Platforms","year":2021,"lang":"en","type":"article","venue":"Information Systems Research","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Incentive; Profiling (computer programming); Payment; Computer science; Business; Microeconomics; Economics; World Wide Web","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":[0.01646197,0.000462735,0.0009147073,0.00149868,0.001257878,0.004003937,0.00116615,0.002595912,0.004524434],"category_scores_gemma":[0.07659096,0.0008315088,0.0004765363,0.001325818,0.003424824,0.005583668,0.002969478,0.00318773,0.0003514652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002508811,"about_ca_system_score_gemma":0.001641207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001793986,"about_ca_topic_score_gemma":0.002470536,"domain_scores_codex":[0.9927021,0.004938735,0.0002165041,0.000751607,0.000778833,0.0006121843],"domain_scores_gemma":[0.8852726,0.08103373,0.02185524,0.006962307,0.002667907,0.002208312],"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":[0.001597368,0.001344583,0.2826901,0.0002875564,0.0002473474,0.001063818,0.003222321,0.1210228,0.005492669,0.5018919,0.00207582,0.07906373],"study_design_scores_gemma":[0.0002128411,0.0007403565,0.08729681,0.0001342882,0.0001205406,0.0005242627,0.002485524,0.5687411,0.002590911,0.3323731,0.004599906,0.0001802523],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9396199,0.0002317838,0.0409088,0.002382449,0.0000231719,0.000179223,0.0001012003,0.00007166598,0.01648181],"genre_scores_gemma":[0.9968289,0.00005017654,0.002278906,0.00006784776,0.00001192087,0.00002336285,0.00001339888,0.000003615799,0.0007219643],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01646197,"threshold_uncertainty_score":0.08706027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1631080809681995,"score_gpt":0.3723639577763807,"score_spread":0.2092558768081812,"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."}}