{"id":"W4394962865","doi":"10.3390/educsci14040429","title":"The Intellectual Evolution of Educational Leadership Research: A Combined Bibliometric and Thematic Analysis Using SciMAT","year":2024,"lang":"en","type":"article","venue":"Education Sciences","topic":"Competency Development and Evaluation","field":"Psychology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Thematic analysis; Thematic map; Educational research; Content analysis; Statistical analysis; Bibliometrics; Sociology; Qualitative research; Mathematics education; Computer science; Psychology; Pedagogy; Library science; Social science; Geography; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics","insufficient_payload"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.00411369,0.00005798656,0.00008476637,0.01867435,0.0005434482,0.0002491559,0.0002236503,0.00002745453,0.001083561],"category_scores_gemma":[0.001559456,0.00003947162,0.00003858501,0.08182719,0.0006389876,0.0002071297,0.00003283039,0.00009039175,0.00008203457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001134547,"about_ca_system_score_gemma":0.001273525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001358087,"about_ca_topic_score_gemma":0.00003852112,"domain_scores_codex":[0.9984294,0.0003013972,0.0002503443,0.0002475506,0.0005806042,0.0001907414],"domain_scores_gemma":[0.9956737,0.003817504,0.00006159155,0.0001380218,0.0002727119,0.00003644146],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00004194512,0.0005756952,0.1092345,0.0002056628,0.0006273985,1.758718e-7,0.1001398,0.000123476,0.001342798,0.5895887,0.02732312,0.1707968],"study_design_scores_gemma":[0.0001453695,0.000203733,0.699924,0.000314348,0.0002763598,0.0000143963,0.1531105,0.0378844,0.0001274936,0.106714,0.001039137,0.0002463201],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9678448,0.01822229,0.0008270899,0.004952775,0.00137489,0.0002019715,7.36876e-7,0.00001092404,0.006564522],"genre_scores_gemma":[0.996742,0.00004993731,0.0004940716,0.0000101153,0.00006565214,0.00003462741,0.000002932851,0.000003048216,0.002597682],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5906895,"threshold_uncertainty_score":0.9998296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5300739037356714,"score_gpt":0.5334121373851421,"score_spread":0.003338233649470723,"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."}}