{"id":"W4389802044","doi":"10.1108/ejtd-10-2023-0152","title":"Systematic bibliometric review of artificial intelligence in human resource development: insights for HRD researchers, practitioners and policymakers","year":2023,"lang":"en","type":"article","venue":"European journal of training and development","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Originality; Scopus; Human resources; Bibliometrics; Knowledge management; Field (mathematics); Engineering ethics; Artificial intelligence; Management science; Psychology; Sociology; Computer science; Management; Social science; Political science; Engineering; Qualitative research","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":[],"consensus_categories":[],"category_scores_codex":[0.00616985,0.0001016266,0.0003911583,0.006210853,0.0001374967,0.00002457533,0.00007906563,0.00002626603,0.000005822841],"category_scores_gemma":[0.002215601,0.00008295477,0.00003352954,0.004910458,0.00008295422,0.00008766676,0.00002927853,0.0001970552,0.000005560068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008279875,"about_ca_system_score_gemma":0.0005173462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004394721,"about_ca_topic_score_gemma":0.000003951015,"domain_scores_codex":[0.9976198,0.0002714653,0.001405409,0.0001436788,0.0003466505,0.0002129979],"domain_scores_gemma":[0.9984909,0.0004700675,0.0004634527,0.00008138524,0.0003099493,0.0001842038],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0001912838,0.0001601276,0.0006821448,0.07879873,0.0001809755,0.0001400573,0.1222857,0.00002493786,0.0004743936,0.0003653977,0.001181794,0.7955145],"study_design_scores_gemma":[0.000850159,0.003497572,0.05645845,0.5433967,0.000284319,0.001027131,0.3159395,0.0005057807,0.01419639,0.001071295,0.06163552,0.001137245],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9748183,0.0175197,0.002860858,0.002672072,0.0001794765,0.001030341,0.000001120673,0.00002706014,0.0008909979],"genre_scores_gemma":[0.9822875,0.005835149,0.01125859,0.0003565849,0.0001174291,0.00001495624,0.0000144263,0.00002453297,0.00009086772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7943773,"threshold_uncertainty_score":0.554188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.469635911945894,"score_gpt":0.4772988938357339,"score_spread":0.007662981889839882,"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."}}