{"id":"W4404612233","doi":"10.1007/s12599-024-00909-z","title":"Correction: Practical Techniques for Theorizing from Literature Reviews","year":2024,"lang":"en","type":"article","venue":"Business & Information Systems Engineering","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Computer science; Information retrieval; Data science; Management science; Engineering","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":[0.05602989,0.006025808,0.006641666,0.01864289,0.007598851,0.01276271,0.0116827,0.0171627,0.09474272],"category_scores_gemma":[0.6626926,0.004949601,0.005127379,0.02200772,0.008079158,0.009194902,0.007747691,0.02627291,0.05604654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01013898,"about_ca_system_score_gemma":0.02138146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01514166,"about_ca_topic_score_gemma":0.02052251,"domain_scores_codex":[0.9083542,0.02689948,0.02644125,0.008964572,0.02653859,0.002801921],"domain_scores_gemma":[0.3414948,0.2186778,0.03192683,0.06608348,0.3337786,0.008038619],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003908431,0.000004700521,0.00006644877,0.000978263,0.00008072817,0.0001149662,0.0001104008,0.00004450714,0.00004019329,0.001231248,0.9925306,0.004758836],"study_design_scores_gemma":[0.0006153228,0.0000422786,0.001377127,0.005744795,0.000619149,0.0007386311,0.0004753604,0.001551796,0.0005781385,0.01832829,0.969709,0.000220237],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"methods","genre_scores_codex":[0.0001066679,0.001602891,0.006338111,0.08473067,0.8998339,0.0002555947,0.003769832,0.001849962,0.001512435],"genre_scores_gemma":[0.0213602,0.008502301,0.09536862,0.2641392,0.4735015,0.007793835,0.00789945,0.009195252,0.1122396],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9439701,"threshold_uncertainty_score":0.316946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3495325840850425,"score_gpt":0.4528177352230406,"score_spread":0.1032851511379981,"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."}}