{"id":"W2107554981","doi":"10.1353/lan.2006.0011","title":"Number Agreement in British and American English: Disagreeing to Agree Collectively","year":2006,"lang":"en","type":"article","venue":"Language","topic":"Syntax, Semantics, Linguistic Variation","field":"Arts and Humanities","cited_by":154,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"University of Pennsylvania; National Institutes of Health; Max-Planck-Gesellschaft; National Science Foundation","keywords":"Notional amount; Linguistics; Noun; Agreement; Verb; Sentence; Predicative expression; Pronoun; Representation (politics); Psychology; Computer science; Economics; Philosophy; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001127433,0.00009132924,0.0001585426,0.00005948099,0.0001039353,0.0002173401,0.00006787626,0.00001710633,0.0005667696],"category_scores_gemma":[0.0002123732,0.0001095262,0.0000224612,0.00007106855,0.00007377034,0.00005993183,0.0000446487,0.00006545927,0.00004678079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006928788,"about_ca_system_score_gemma":0.0000108502,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.139771,"about_ca_topic_score_gemma":0.4272597,"domain_scores_codex":[0.9992289,0.0000316952,0.0001826102,0.000205859,0.0001438403,0.0002071012],"domain_scores_gemma":[0.999661,0.00006672312,0.0000601845,0.000106399,0.00006638951,0.00003924798],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.00003586982,0.0002968897,0.1278824,0.00006664362,0.00005066413,0.0003474508,0.6879095,0.00002677221,0.000266489,0.1554199,0.003047515,0.02464993],"study_design_scores_gemma":[0.002014101,0.0002036973,0.7281368,0.0003391013,0.00007424368,0.00002008365,0.221824,0.00037823,0.0003159881,0.01827008,0.0273077,0.001115933],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9376011,0.00003755113,0.00007531103,0.0006575511,0.0003144635,0.0001914489,0.0000366206,0.00005016096,0.06103573],"genre_scores_gemma":[0.9936493,0.00000203313,0.0005705057,0.0001965988,0.001213993,0.00001698361,0.00001721818,0.00001586563,0.004317471],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6002545,"threshold_uncertainty_score":0.8659573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008580289320768843,"score_gpt":0.2241797295650044,"score_spread":0.2155994402442356,"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."}}