{"id":"W2898519004","doi":"","title":"La configuración institucional de las comisiones de mérito en los procesos selectivos: experiencias comparadas y aplicación al contexto español","year":2018,"lang":"es","type":"article","venue":"Pertsonak eta Antolakunde Publikoak Kudeatzeko Euskal Aldizkaria = Revista Vasca de Gestión de Personas y Organizaciones Públicas","topic":"Finance, Taxation, and Governance","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Context (archaeology); Welfare economics; Geography; Philosophy; Economics","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":[0.003572647,0.002215711,0.002174256,0.0005890151,0.002625422,0.002696918,0.003385466,0.001671274,0.007267778],"category_scores_gemma":[0.00446004,0.002279558,0.0007230155,0.00342941,0.003048486,0.002543564,0.0009953566,0.002227338,0.001355139],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003993995,"about_ca_system_score_gemma":0.003344076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003237915,"about_ca_topic_score_gemma":0.001835445,"domain_scores_codex":[0.9863551,0.001990698,0.00198613,0.003316825,0.00249032,0.003860899],"domain_scores_gemma":[0.9920547,0.001545404,0.00150913,0.001820055,0.0008931165,0.002177604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001510516,0.00315858,0.6789876,0.000954842,0.0007012695,0.0004703037,0.03332821,0.0001894412,0.04261268,0.09885032,0.1301304,0.009105929],"study_design_scores_gemma":[0.002757911,0.0007089838,0.3845209,0.0007312894,0.0003545203,0.003184232,0.00271117,0.007292064,0.004163034,0.0004828412,0.5907946,0.002298424],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9556332,0.005560842,0.009320429,0.01776344,0.0005006162,0.002110678,0.0007837462,0.0008013017,0.007525716],"genre_scores_gemma":[0.980042,0.002660458,0.006031171,0.004579885,0.001793907,0.000396295,0.0002063446,0.0004124854,0.003877447],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4606642,"threshold_uncertainty_score":0.9998295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01484834683582387,"score_gpt":0.3005104570842717,"score_spread":0.2856621102484478,"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."}}