{"id":"W3004407118","doi":"10.1108/ejtd-09-2019-0161","title":"Analysis of national human resource development (NHRD) policies of 2016 in South Korea with implications","year":2020,"lang":"en","type":"article","venue":"European journal of training and development","topic":"Retirement, Disability, and Employment","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Human resources; Development plan; Government (linguistics); Policy analysis; Public policy; Originality; Central government; Business; Public administration; Public economics; Local government; Economics; Political science; Economic growth; Engineering; Management","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":[],"consensus_categories":[],"category_scores_codex":[0.007487987,0.0002907291,0.0002366544,0.002625724,0.001255834,0.00247412,0.0006286231,0.0002815338,0.00279095],"category_scores_gemma":[0.01046432,0.0002552178,0.0003463586,0.006012469,0.000862125,0.002041778,0.00201888,0.0009108429,0.0002383796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01929267,"about_ca_system_score_gemma":0.02874387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0749594,"about_ca_topic_score_gemma":0.08734474,"domain_scores_codex":[0.9962425,0.001242745,0.0006137873,0.0004420607,0.0007229734,0.0007359171],"domain_scores_gemma":[0.9898956,0.002604433,0.002682721,0.0005941585,0.003327117,0.0008959688],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0004873654,0.0007526717,0.559378,0.006038156,0.0003590945,0.001231898,0.02753237,0.01868935,0.003844709,0.0528115,0.02658519,0.3022897],"study_design_scores_gemma":[0.00003061452,0.0002079972,0.8305971,0.001333705,0.0001105881,0.0001105617,0.07409638,0.004975224,0.002419793,0.003601409,0.082435,0.0000815746],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9617639,0.00117891,0.002774286,0.005162975,0.00006839883,0.000776467,0.004191385,0.00008561481,0.02399813],"genre_scores_gemma":[0.9841368,0.001036664,0.00552579,0.0007199738,0.00001168713,0.000623535,0.0022124,0.00003084421,0.005702268],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0749594,"threshold_uncertainty_score":0.1490462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3006031702885459,"score_gpt":0.3870194755114433,"score_spread":0.0864163052228974,"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."}}