{"id":"W3001060432","doi":"10.5267/j.msl.2019.11.042","title":"Effect of recruitment, selection and culture of organizations on state personnel performance","year":2019,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Socioeconomic Development in MENA","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Business; Organizational culture; State (computer science); Personnel selection; Psychology; Operations management; Knowledge management; Public relations; Management; Computer science; Political science; Engineering; Artificial intelligence; Economics","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":[0.001335504,0.00007242756,0.000104899,0.0001649118,0.0002882352,0.00003635767,0.0002426712,0.00001677775,0.00007191837],"category_scores_gemma":[0.00004266206,0.00006438507,0.00001246889,0.0009069203,0.0003420718,0.0003017955,0.00005513519,0.00004807592,0.0000347863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001629443,"about_ca_system_score_gemma":0.00001773469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004559444,"about_ca_topic_score_gemma":0.000005347244,"domain_scores_codex":[0.9990372,0.00006725021,0.0001281473,0.0002253529,0.0003397115,0.000202338],"domain_scores_gemma":[0.9996682,0.00004541535,0.0001151416,0.0001033883,0.00003187686,0.00003596245],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004787522,0.0000568318,0.832735,0.0003552337,0.00006873812,7.127865e-7,0.1011003,0.001643885,0.02859771,0.003935065,0.00248966,0.02896902],"study_design_scores_gemma":[0.005534087,0.002951402,0.6874153,0.0008207152,0.0002204589,0.000002359672,0.05197738,0.004387707,0.2000761,0.0003817184,0.04449435,0.001738444],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885889,0.000003461806,0.00007006384,0.0006377156,0.0002372421,0.0006782727,8.030948e-7,0.00002182181,0.009761717],"genre_scores_gemma":[0.9973792,0.00008217259,0.0006114787,0.0003641127,0.00001150535,0.00001624968,9.749798e-7,0.00000456175,0.001529743],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1714784,"threshold_uncertainty_score":0.2625547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009801512717873056,"score_gpt":0.2720436926206216,"score_spread":0.2622421799027486,"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."}}