{"id":"W7143875396","doi":"10.34577/0002000287","title":"研究提言：カナダの研究者による研究不正の分析査読論文撤回を 対象とした研究","year":2025,"lang":"","type":"article","venue":"Institutional Repositories DataBase (IRDB)","topic":"Academic integrity and plagiarism","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Misconduct; Scientific misconduct; Rigour; Scientific literature; Scientific evidence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.05730389,0.00034699,0.001012427,0.0225473,0.005926107,0.01087804,0.001961861,0.0008920073,0.009477607],"category_scores_gemma":[0.1936661,0.0003991943,0.0006428503,0.0318308,0.006689926,0.007898958,0.003244908,0.001663499,0.002576806],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01373554,"about_ca_system_score_gemma":0.03349273,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09380581,"about_ca_topic_score_gemma":0.1784348,"domain_scores_codex":[0.9371224,0.01285605,0.008235021,0.004294231,0.03567733,0.001815018],"domain_scores_gemma":[0.6118463,0.1685942,0.07143336,0.02094427,0.1209377,0.006244183],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002579303,0.0001596964,0.3911129,0.007016952,0.0006058521,0.00070575,0.02830415,0.0006573221,0.003783857,0.05577315,0.02480527,0.4868172],"study_design_scores_gemma":[0.00004532641,0.0002540526,0.6084023,0.005549093,0.0008741523,0.002036266,0.04994205,0.002281897,0.01247319,0.0546823,0.2631826,0.000276691],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5177035,0.03264514,0.04697695,0.07405242,0.001658044,0.001625525,0.01678134,0.0007463379,0.3078108],"genre_scores_gemma":[0.9391846,0.01174197,0.02887741,0.003124263,0.0005849943,0.0003967507,0.003076504,0.0001571406,0.01285645],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.999108,"threshold_uncertainty_score":0.3030556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01806422344481797,"score_gpt":0.3118366118131577,"score_spread":0.2937723883683398,"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."}}