{"id":"W3013557474","doi":"10.5430/ijhe.v9n3p183","title":"Corporate Training Programs in Russian and Foreign Companies: Impact on Staff and Time Challenges","year":2020,"lang":"en","type":"article","venue":"International Journal of Higher Education","topic":"Human Resources and Workforce","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Training (meteorology); Business; Process (computing); Marketing; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001520821,0.0002107028,0.0001137116,0.0005774614,0.001546632,0.001192879,0.0007276023,0.0008433886,0.007216392],"category_scores_gemma":[0.003442189,0.00009118002,0.0001813707,0.0004417965,0.0004657713,0.0005424204,0.001908952,0.0007686689,0.0007057862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002446841,"about_ca_system_score_gemma":0.00674939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01078941,"about_ca_topic_score_gemma":0.01662116,"domain_scores_codex":[0.9986084,0.0005577991,0.00002687692,0.00008718442,0.0001373792,0.0005823217],"domain_scores_gemma":[0.9921553,0.0007728599,0.000497082,0.00009193154,0.0006645516,0.00581832],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00189142,0.005209035,0.2922052,0.0008739167,0.00006252839,0.00193175,0.01996433,0.002741701,0.00509402,0.01064753,0.03722977,0.6221488],"study_design_scores_gemma":[0.0002060706,0.003458635,0.8398402,0.0009496322,0.00005645855,0.0007010829,0.06471531,0.001744888,0.001753939,0.001819005,0.08470479,0.00005008123],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9767764,0.001828449,0.0001884589,0.005592675,0.00033696,0.0000544244,0.0001760615,0.0000423907,0.01500421],"genre_scores_gemma":[0.9897435,0.0008053662,0.0001733115,0.0006414702,0.0001046078,0.00003979571,0.0001216757,0.000008766645,0.008361519],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01078941,"threshold_uncertainty_score":0.02414125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1189179221991099,"score_gpt":0.3603926727973127,"score_spread":0.2414747505982028,"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."}}