{"id":"W2148386031","doi":"10.1162/ling.2006.37.2.329","title":"Raising to Object in Japanese: A Small Clause Analysis","year":2006,"lang":"en","type":"article","venue":"Linguistic Inquiry","topic":"Syntax, Semantics, Linguistic Variation","field":"Arts and Humanities","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Icon; Citation; Download; Computer science; Object (grammar); Raising (metalworking); Linguistics; Information retrieval; Library science; Filter (signal processing); World Wide Web; Artificial intelligence; Mathematics; Programming language","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"],"consensus_categories":[],"category_scores_codex":[0.0005109881,0.0002540678,0.0004536168,0.0008682862,0.0002217308,0.0002976323,0.0002387492,0.00008805267,0.0004994412],"category_scores_gemma":[0.002868755,0.0002630204,0.0001544943,0.000401223,0.0001375508,0.00003587188,0.00007308718,0.000187515,0.0002401353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001418663,"about_ca_system_score_gemma":0.00005546032,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02353887,"about_ca_topic_score_gemma":0.0401054,"domain_scores_codex":[0.9980061,0.00008854739,0.0006755143,0.0004676825,0.0002871849,0.0004749381],"domain_scores_gemma":[0.9986293,0.0003465798,0.0001660659,0.0004358179,0.0003400597,0.00008215158],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008072705,0.0003095697,0.01126858,0.00009976799,0.0002906302,0.0002249653,0.3813547,0.002860138,0.00010189,0.6025175,0.0003982818,0.000493128],"study_design_scores_gemma":[0.005023474,0.0006249113,0.1341676,0.0009844376,0.00545624,0.00002683805,0.05814064,0.02837251,0.0005987544,0.6794763,0.08184912,0.005279192],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9179404,0.00008517601,0.004507686,0.001523249,0.006381012,0.0004605816,0.00003544643,0.0002710883,0.06879538],"genre_scores_gemma":[0.9870019,9.52326e-7,0.00124816,0.0003465571,0.008950192,0.00002429357,0.00008996082,0.00004042582,0.002297537],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3232141,"threshold_uncertainty_score":0.9999822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05999699038156184,"score_gpt":0.2845633775433342,"score_spread":0.2245663871617724,"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."}}