{"id":"W2773728013","doi":"10.1111/1911-3838.12154","title":"From Plagiarism‐Plagued to Plagiarism‐Proof: Using Anonymized Case Assignments in Intermediate Accounting","year":2017,"lang":"en","type":"article","venue":"Accounting Perspectives","topic":"Academic integrity and plagiarism","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Rationalization (economics); Misconduct; Academic integrity; Computer science; Cheating; Mathematics education; Psychology; Accounting; Business; Political science; Social psychology; Library science; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["research_integrity"],"domain":null,"study_design":"design_other","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.02971035,0.0004278781,0.0003246583,0.001852067,0.004180985,0.007365815,0.001814457,0.001247931,0.003198817],"category_scores_gemma":[0.1546578,0.0005020763,0.0003013912,0.001072415,0.00517429,0.005028063,0.006143004,0.002657326,0.0006815355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002737003,"about_ca_system_score_gemma":0.002204799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000743637,"about_ca_topic_score_gemma":0.001619618,"domain_scores_codex":[0.945228,0.04543895,0.001950485,0.002199749,0.004134663,0.001048176],"domain_scores_gemma":[0.7857079,0.1590448,0.02115715,0.02149968,0.008331644,0.004258778],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001968571,0.007103964,0.06266501,0.0006566406,0.0001103339,0.002758245,0.3147207,0.007196771,0.01634767,0.026472,0.005360321,0.5546399],"study_design_scores_gemma":[0.001074131,0.0181375,0.1622438,0.003936264,0.0003917919,0.007828593,0.3019708,0.09466954,0.143159,0.1130475,0.1521241,0.001417025],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9722339,0.00007188819,0.0188689,0.0009916603,0.00005273516,0.0004437861,0.00002623203,0.0002273728,0.00708352],"genre_scores_gemma":[0.9836227,0.00005695464,0.01476407,0.0001660233,0.00001692753,0.000168667,0.00002080549,0.00003185677,0.001151966],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9987521,"threshold_uncertainty_score":0.1571252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03570606692757128,"score_gpt":0.3614844814002403,"score_spread":0.325778414472669,"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."}}