{"id":"W4296762334","doi":"10.31222/osf.io/9we43","title":"Reducing the residue of retractions in evidence synthesis: Ways to minimize inappropriate citation and use of retracted data","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Academic integrity and plagiarism","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Alfred P. Sloan Foundation","keywords":"Citation; Systematic review; Medical literature; MEDLINE; Evidence-based medicine; Computer science; Data science; Psychology; Medicine; Political science; Library science; Law","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":["metaresearch","research_integrity"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.8032016,0.005932501,0.01162077,0.04016271,0.01155649,0.03437399,0.01627997,0.02428564,0.01420806],"category_scores_gemma":[0.9565777,0.009908392,0.0115651,0.03880198,0.0223549,0.04491773,0.03047355,0.0268501,0.01151974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01285743,"about_ca_system_score_gemma":0.06174533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003567316,"about_ca_topic_score_gemma":0.004484558,"domain_scores_codex":[0.1258764,0.5984061,0.1823778,0.0192895,0.07105024,0.002999966],"domain_scores_gemma":[0.01552096,0.7409848,0.08120127,0.09553481,0.06393078,0.002827427],"domain_codex":"methods","domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002371646,0.0005000727,0.008835942,0.02270034,0.003342122,0.001028449,0.03511621,0.005056436,0.002769549,0.04285378,0.06507358,0.8103518],"study_design_scores_gemma":[0.004528199,0.002179425,0.01733649,0.07218467,0.007535363,0.002825004,0.01515656,0.03769276,0.01456514,0.4651707,0.3586768,0.002148898],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01347603,0.0207177,0.7565289,0.1579397,0.01582289,0.01769304,0.001118628,0.005002219,0.01170096],"genre_scores_gemma":[0.07719085,0.006910223,0.8694184,0.01773794,0.006774328,0.01463613,0.000775368,0.002033006,0.004523738],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9757144,"threshold_uncertainty_score":0.2426875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2284432204022186,"score_gpt":0.375603707732273,"score_spread":0.1471604873300544,"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."}}