{"id":"W3094744592","doi":"10.5206/cjsotl-rcacea.2020.2.8508","title":"Examining Academic Integrity Using Course-Level Learning Outcomes","year":2020,"lang":"en","type":"article","venue":"The Canadian Journal for the Scholarship of Teaching and Learning","topic":"Academic integrity and plagiarism","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Academic integrity; Academic dishonesty; Learning development; Academic achievement; Higher education; Mathematics education; Data integrity; Audit; Psychology; Medical education; Computer science; Political science; Medicine; Management; Computer security; Social psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.02646424,0.00016344,0.0002617124,0.00008124531,0.01554954,0.0004137769,0.0008098263,0.0004855486,0.00005289662],"category_scores_gemma":[0.03056533,0.0001061168,0.0001294115,0.0001494197,0.0004863231,0.0004416903,0.00004116771,0.03113145,0.000003427855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001470604,"about_ca_system_score_gemma":0.001176358,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03908078,"about_ca_topic_score_gemma":0.00912542,"domain_scores_codex":[0.9952235,0.003263406,0.000358153,0.0001740803,0.0004093414,0.0005714734],"domain_scores_gemma":[0.9959518,0.002878703,0.0003780464,0.00008338291,0.0001560952,0.0005519771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.00005621285,0.000005103781,0.5628997,0.00003813567,0.0002781927,0.00001036807,0.2884592,0.00614645,0.0003648484,0.02858967,0.0005828781,0.1125692],"study_design_scores_gemma":[0.001284366,0.0003694595,0.0946679,0.0009636271,0.000785153,0.0002227289,0.4559652,0.02060684,0.00005039249,0.0124796,0.4115995,0.00100522],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9481118,0.002012489,0.003885051,0.04363504,0.0005681144,0.0002248024,0.000007654866,0.00003694288,0.001518074],"genre_scores_gemma":[0.9957126,0.00007415847,0.001017854,0.0012663,0.0007437316,0.000001758611,0.000001342989,0.00002230073,0.001159904],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4682318,"threshold_uncertainty_score":0.9857321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2472324269926388,"score_gpt":0.3901059812827297,"score_spread":0.1428735542900909,"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."}}