{"id":"W3041079116","doi":"10.1037/met0000422","title":"Parsimony in model selection: Tools for assessing fit propensity.","year":2021,"lang":"en","type":"preprint","venue":"Psychological Methods","topic":"Mental Health Research Topics","field":"Psychology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Model selection; Occam's razor; Computer science; Range (aeronautics); Econometrics; Equating; Statistical model; Statistical inference; Selection (genetic algorithm); Counterfactual conditional; Inference; Statistics; Mathematics; Machine learning; Artificial intelligence; Counterfactual thinking; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.1003576,0.005691072,0.004204773,0.02022631,0.002550032,0.008657505,0.004763812,0.004194328,0.01734917],"category_scores_gemma":[0.4481784,0.002226634,0.007271533,0.01806249,0.004556977,0.009117061,0.00962619,0.009362597,0.003402871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002219238,"about_ca_system_score_gemma":0.004460807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002478177,"about_ca_topic_score_gemma":0.003570151,"domain_scores_codex":[0.9344133,0.05095588,0.00426575,0.003680773,0.006148044,0.0005364182],"domain_scores_gemma":[0.5170145,0.4440529,0.01182667,0.01868357,0.006986638,0.001435729],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006684912,0.000487353,0.05832814,0.006157413,0.009102545,0.001338604,0.006516792,0.07859029,0.001453228,0.2753175,0.06953854,0.4925011],"study_design_scores_gemma":[0.0002560691,0.0002475774,0.008125499,0.001702652,0.001160527,0.0007064778,0.001220028,0.297855,0.00107503,0.6649706,0.02238598,0.0002946357],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005571193,0.001124883,0.9837573,0.00130283,0.0001320887,0.0006531931,0.001767353,0.003063681,0.002627397],"genre_scores_gemma":[0.1049401,0.0009242592,0.8843394,0.0006543481,0.0001927394,0.003801035,0.002317276,0.002130872,0.000700002],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1003576,"threshold_uncertainty_score":0.5307482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7544020784369739,"score_gpt":0.6733340687936658,"score_spread":0.08106800964330818,"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."}}