{"id":"W3120304548","doi":"10.5430/rwe.v12n1p156","title":"Employing Quantile Regression for Influences of Human Resource Management on Employee Performance","year":2021,"lang":"en","type":"article","venue":"Research in World Economy","topic":"Innovation and Knowledge Management","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Human resource management; Quantile regression; Business; Quantile; Empirical research; Resource (disambiguation); Resource management (computing); Human resources; Regression analysis; Knowledge management; Marketing; Industrial organization; Economics; Computer science; Econometrics; Management; Statistics; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001873011,0.0001404323,0.0002274726,0.00153809,0.0003199689,0.0002066743,0.0003532675,0.00003427862,0.0001915938],"category_scores_gemma":[0.00003794674,0.0001361562,0.00006260502,0.001643408,0.00008354634,0.0004299744,0.0004128529,0.0002194654,0.0001535567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009711635,"about_ca_system_score_gemma":0.00002184338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002456956,"about_ca_topic_score_gemma":0.0001773019,"domain_scores_codex":[0.9983398,0.00003386571,0.0004854678,0.0004038157,0.0002721355,0.00046492],"domain_scores_gemma":[0.999027,0.0001084815,0.0001413917,0.0003973381,0.0003121246,0.00001365299],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001067565,0.0002341531,0.1105403,0.00220799,0.00004371959,0.00002036251,0.00007067199,0.0001146577,0.0001255196,0.8528024,0.0124888,0.02124467],"study_design_scores_gemma":[0.001793763,0.00005909922,0.04540211,0.001581065,0.00001393321,3.238565e-7,0.002225791,0.001589035,0.00220268,0.01686325,0.9278812,0.0003876889],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3712323,0.00004029307,0.000004907756,0.0007041753,0.00008252395,0.0004682923,6.777991e-7,0.00002996165,0.6274368],"genre_scores_gemma":[0.989646,0.0000102338,0.0002031758,0.0005151412,0.0003141267,0.0002082425,0.00003518073,0.00002487145,0.009043085],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9153925,"threshold_uncertainty_score":0.5552289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1310628276903309,"score_gpt":0.3771259728386791,"score_spread":0.2460631451483482,"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."}}