{"id":"W7020986516","doi":"","title":"Names and Naming. People, Places, Perceptions and Power
\\n[Nevek és névadás. Emberek, helyek, felfogások és hatalom]. 
\\nSzerkesztők: Guy Puzey – Laura Kostanski. Multilingual Matters, Bristol–Buffalo–Toronto, 2016. 258 lap","year":2017,"lang":"hu","type":"article","venue":"Repository of the Academy's Library (Library of the Hungarian Academy of Sciences)","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Perception; Feature (linguistics); Identity (music)","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":[0.0007765316,0.000411232,0.0002209352,0.001152651,0.001906827,0.00489996,0.0003763436,0.0009968873,0.04102187],"category_scores_gemma":[0.003289416,0.0002502631,0.0001449252,0.003415583,0.003942838,0.007524919,0.001566933,0.0008826328,0.006958591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001478006,"about_ca_system_score_gemma":0.001291966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04242028,"about_ca_topic_score_gemma":0.09512307,"domain_scores_codex":[0.9995041,0.0002571953,0.00002949505,0.00004147325,0.0001118466,0.00005586054],"domain_scores_gemma":[0.9989516,0.0005437203,0.0001434892,0.00008159246,0.0001572096,0.0001224485],"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":[0.00004921538,0.00002372603,0.006639598,0.0003900952,0.00001227008,0.0001386952,0.04851081,0.00008405017,0.0001911546,0.0893672,0.5952201,0.259373],"study_design_scores_gemma":[0.000008192818,0.0000157159,0.02531395,0.0006569885,0.000009936047,0.0002251352,0.06331799,0.00006625607,0.0001411884,0.03312627,0.8770945,0.0000238272],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"review","genre_scores_codex":[0.02449764,0.2316842,0.003173504,0.1339662,0.00608801,0.00003481404,0.002759899,0.0003473251,0.5974484],"genre_scores_gemma":[0.526859,0.1337072,0.002307287,0.008295986,0.002378435,0.000132239,0.001817474,0.0003194438,0.3241829],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.04242028,"threshold_uncertainty_score":0.1372318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02759679731551107,"score_gpt":0.2944111023188363,"score_spread":0.2668143050033252,"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."}}