{"id":"W2165746155","doi":"10.1007/s11156-009-0123-1","title":"Binary response and logistic regression in recent accounting research publications: a methodological note","year":2009,"lang":"en","type":"article","venue":"Review of Quantitative Finance and Accounting","topic":"Statistical Methods and Applications","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; University of Manitoba","funders":"","keywords":"Logistic regression; Regression diagnostic; Econometrics; Regression analysis; Presentation (obstetrics); Ordinary least squares; Linear regression; Variables; Statistics; Accounting; Computer science; Mathematics; Economics; Polynomial regression; Medicine","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"],"consensus_categories":[],"category_scores_codex":[0.07347815,0.0006269209,0.00122286,0.01929511,0.002003694,0.01249438,0.002477801,0.002865514,0.004845037],"category_scores_gemma":[0.1936662,0.0006500061,0.001175122,0.04497007,0.005817441,0.01066945,0.003450851,0.002734346,0.001325108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001928637,"about_ca_system_score_gemma":0.005025247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00282253,"about_ca_topic_score_gemma":0.005288084,"domain_scores_codex":[0.9417374,0.03954071,0.005066343,0.004335242,0.008495684,0.0008246276],"domain_scores_gemma":[0.5768071,0.349689,0.02404681,0.01247603,0.03571568,0.00126535],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003166772,0.0001695628,0.05765996,0.01082725,0.0007773772,0.0004404198,0.005378011,0.0008062033,0.001236294,0.3948678,0.05051242,0.4770081],"study_design_scores_gemma":[0.0001633624,0.0003220744,0.09247513,0.0158413,0.001994692,0.002724621,0.01010965,0.006427766,0.004060596,0.3368779,0.5287039,0.000298964],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"methods","genre_scores_codex":[0.04084527,0.619585,0.1664321,0.1362369,0.009038791,0.0002147458,0.002240155,0.0002228827,0.02518421],"genre_scores_gemma":[0.4069255,0.3721826,0.1376745,0.02738078,0.0352519,0.001087308,0.002216143,0.0005242521,0.01675701],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9265218,"threshold_uncertainty_score":0.3885942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.638020466979831,"score_gpt":0.6192974701062952,"score_spread":0.01872299687353574,"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."}}