{"id":"W2275805726","doi":"","title":"ИНФОРМАЦИОННЫЙ МЕНЕДЖМЕНТ И УПРАВЛЕНИЕ ГОСУДАРСТВЕННОЙ ИНФОРМАЦИЕЙ КАК КОМПОНЕНТЫ ГОСУДАРСТВЕННОЙ ИНФОРМАЦИОННОЙ ПОЛИТИКИ КАНАДЫ","year":2013,"lang":"ru","type":"article","venue":"Исторические, философские, политические и юридические науки, культурология и искусствоведение. Вопросы теории и практики","topic":"Information Architecture and Usability","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Realia; Politics; Administration (probate law); Political science; Information policy; Public administration; Public relations; Computer science; Law; Library 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004541367,0.0003702276,0.0003675792,0.002808206,0.002848884,0.00770359,0.0008051455,0.001435065,0.01189964],"category_scores_gemma":[0.01290923,0.0005850811,0.0003988183,0.003216426,0.006494337,0.004317458,0.002471076,0.001641672,0.00290232],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003803616,"about_ca_system_score_gemma":0.008620652,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01426078,"about_ca_topic_score_gemma":0.01464911,"domain_scores_codex":[0.9934554,0.001547401,0.0002803697,0.0006045267,0.003638322,0.0004740463],"domain_scores_gemma":[0.9924169,0.003491253,0.0009195855,0.001091177,0.00159968,0.0004813771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00007896463,0.00008112917,0.003798358,0.0003171929,0.00002489688,0.0002778988,0.005910246,0.001472025,0.006414554,0.8439472,0.005511046,0.1321666],"study_design_scores_gemma":[0.00006445799,0.000123986,0.00736304,0.0003580721,0.00005894377,0.000786615,0.0051772,0.002689991,0.01099164,0.4084969,0.5637746,0.000114738],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.08953025,0.008945371,0.1288188,0.01445634,0.0006503109,0.000297098,0.0004153267,0.0005218192,0.7563648],"genre_scores_gemma":[0.8953372,0.005144136,0.05989627,0.0005321592,0.0001869855,0.0002837885,0.000166887,0.0001918369,0.03826077],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9961964,"threshold_uncertainty_score":0.03980827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01063118814037715,"score_gpt":0.2247455490812726,"score_spread":0.2141143609408954,"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."}}