{"id":"W4400482819","doi":"10.55016/ojs/cpai.v4i1.72824","title":"Proactive not punitive: Approaching academic integrity from an educational perspective","year":2021,"lang":"en","type":"article","venue":"Canadian Perspectives on Academic Integrity","topic":"Academic integrity and plagiarism","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saskatchewan Polytechnic; University of Saskatchewan","funders":"","keywords":"Punitive damages; Perspective (graphical); Academic integrity; Engineering ethics; Psychology; Political science; Computer science; Engineering; Law; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.003565662,0.0007142011,0.0007374613,0.0007028003,0.002423969,0.0002317595,0.001706898,0.00481411,0.003115669],"category_scores_gemma":[0.01520115,0.0007751305,0.000346163,0.00101309,0.001732677,0.002262682,0.0001302676,0.04288907,0.0004145468],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.01080137,"about_ca_system_score_gemma":0.01791232,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6093589,"about_ca_topic_score_gemma":0.2792858,"domain_scores_codex":[0.9910487,0.003154963,0.000711998,0.002053751,0.001364936,0.001665644],"domain_scores_gemma":[0.9927977,0.001862405,0.0003437916,0.0006625644,0.002230566,0.002102967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001190588,0.0001442555,0.002493355,0.000005123004,0.0001729442,0.00003364901,0.2169966,0.00000453134,0.0002886017,0.7730346,0.004865961,0.001841343],"study_design_scores_gemma":[0.0005627332,0.0000941347,0.02164238,0.0002245472,0.0001195786,0.00003180025,0.7730836,0.0002653355,0.001654233,0.162058,0.03905889,0.001204699],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4085869,0.002704401,0.0007329521,0.2556524,0.00227788,0.001538057,0.002575144,0.0003838305,0.3255485],"genre_scores_gemma":[0.9766546,0.001367452,0.001255234,0.01079984,0.004519253,0.0001301945,0.0003151908,0.00007835059,0.004879831],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6109766,"threshold_uncertainty_score":0.9994699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05648466931229684,"score_gpt":0.3693229868471757,"score_spread":0.3128383175348788,"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."}}