{"id":"W2169106478","doi":"10.3765/exabs.v0i0.3001","title":"Syntactic categories informing variationist analysis: The case of English copy-raising","year":2015,"lang":"en","type":"article","venue":"LSA Annual Meeting Extended Abstracts","topic":"Syntax, Semantics, Linguistic Variation","field":"Arts and Humanities","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Raising (metalworking); Linguistics; Subject (documents); Verb; Variation (astronomy); Transformation (genetics); Value (mathematics); Computer science; Mathematics; Philosophy; Statistics; Physics; Geometry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001631277,0.0002333842,0.0003788428,0.0002361411,0.0006544698,0.0005010271,0.0002329114,0.00008893848,0.00006283775],"category_scores_gemma":[0.01086896,0.0001863608,0.0001378304,0.0002242941,0.0002312714,0.0005470747,0.00007379353,0.0002633496,0.00002233962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007765152,"about_ca_system_score_gemma":0.0001718335,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01579191,"about_ca_topic_score_gemma":0.01012636,"domain_scores_codex":[0.9979956,0.0001209908,0.0008583743,0.0002738463,0.0003979977,0.0003532412],"domain_scores_gemma":[0.9954503,0.0007924332,0.0008363443,0.0004017221,0.002395192,0.0001240283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0000615301,0.0001548817,0.000429727,0.0001020712,0.0009053794,0.000244988,0.5846035,0.004113746,0.00001331023,0.4068832,0.0002440725,0.002243662],"study_design_scores_gemma":[0.00160617,0.0003289822,0.007454374,0.0003228135,0.005797241,0.0002276369,0.7905198,0.005205695,0.001565278,0.1706589,0.01498094,0.001332187],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8241903,0.0002197467,0.0006060923,0.00355084,0.007406585,0.0006035115,0.0002290577,0.0004152736,0.1627786],"genre_scores_gemma":[0.9969077,0.000002200821,0.0005445069,0.00008503209,0.002078832,0.00001147986,0.00005023955,0.00002698438,0.000292991],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2362243,"threshold_uncertainty_score":0.9974629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02936533622308817,"score_gpt":0.2665150480763824,"score_spread":0.2371497118532942,"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."}}