{"id":"W2782971888","doi":"","title":"Proyecciones estratégicas en bibliotecas públicas: qué, cómo y para qué. Un estudio comparativo","year":2018,"lang":"es","type":"article","venue":"Bibliotecas","topic":"Social Sciences and Policies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Originality; Convergence (economics); Library science; Strategic management; Strategic planning; Political science; Value (mathematics); Sociology; Management; Qualitative research; Social science; Computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03266641,0.0005936524,0.001121293,0.01085357,0.005089486,0.02236203,0.001943209,0.001943281,0.004446433],"category_scores_gemma":[0.09171873,0.0006289239,0.0007380026,0.02599576,0.009492454,0.02068788,0.01026989,0.002418058,0.0005233206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01560392,"about_ca_system_score_gemma":0.02267618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02194042,"about_ca_topic_score_gemma":0.0265359,"domain_scores_codex":[0.9568714,0.02634991,0.002194041,0.002247757,0.009021414,0.003315473],"domain_scores_gemma":[0.9420843,0.03312173,0.009569992,0.002491081,0.01110119,0.001631705],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.000702848,0.000575985,0.180315,0.005383873,0.0003614934,0.001316929,0.3848094,0.001755216,0.0008455829,0.1665344,0.004005076,0.2533942],"study_design_scores_gemma":[0.00006536821,0.0003398494,0.1343135,0.004933903,0.0002202511,0.0005385872,0.7745653,0.0008245624,0.0009222648,0.01794725,0.06523403,0.00009524198],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8276761,0.0231709,0.003541562,0.0116921,0.0001942919,0.0003601421,0.0004012562,0.0001162867,0.1328474],"genre_scores_gemma":[0.9938447,0.003511707,0.001159272,0.0002191615,0.00004469052,0.0001128958,0.00008272802,0.0000185041,0.001006304],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03266641,"threshold_uncertainty_score":0.1727586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06107506354486194,"score_gpt":0.3954286988452105,"score_spread":0.3343536353003486,"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."}}