{"id":"W4410798886","doi":"10.34925/eip.2025.178.5.177","title":"Развитие кадрового потенциала государственной гражданской службы: опыт России и зарубежных стран","year":2025,"lang":"ru","type":"article","venue":"Экономика и предпринимательство","topic":"Legal and Regulatory Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","sts","insufficient_payload"],"category_scores_codex":[0.003615857,0.001817486,0.002568199,0.001358226,0.004321781,0.001324345,0.003526936,0.001997944,0.01024378],"category_scores_gemma":[0.001013415,0.001726403,0.002319222,0.007385105,0.003123892,0.001559142,0.001161231,0.002099578,0.005209072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001214759,"about_ca_system_score_gemma":0.00279403,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0122057,"about_ca_topic_score_gemma":0.008174215,"domain_scores_codex":[0.9862688,0.001770534,0.002711615,0.003028706,0.002707979,0.003512331],"domain_scores_gemma":[0.9928138,0.000900506,0.001142231,0.002727294,0.0009700902,0.001446064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008073012,0.003993224,0.0488138,0.001585044,0.006445995,0.0007440237,0.02404953,0.0007592299,0.003351204,0.3693289,0.4584276,0.08169417],"study_design_scores_gemma":[0.002398983,0.0002551522,0.01214434,0.0008641913,0.002202958,0.00001240125,0.008524835,0.0008633156,0.001682566,0.01537428,0.9532011,0.002475897],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2369609,0.01925988,0.00106188,0.04712643,0.0213885,0.003014993,0.0004411461,0.001751388,0.6689948],"genre_scores_gemma":[0.6971179,0.001902652,0.0003708341,0.003640671,0.002893543,0.0001266978,0.0001032522,0.0001388244,0.2937057],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.4947735,"threshold_uncertainty_score":0.9997123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00746119984347639,"score_gpt":0.3009223052714438,"score_spread":0.2934611054279674,"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."}}