{"id":"W3016113440","doi":"10.1017/s1360674320000076","title":"<i>Be like</i>and the Constant Rate Effect: from the bottom to the top of the<i>S</i>-curve","year":2020,"lang":"en","type":"article","venue":"English Language and Linguistics","topic":"Linguistic Variation and Morphology","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Categorical variable; Context (archaeology); Variation (astronomy); Constant (computer programming); Logistic regression; TRACE (psycholinguistics); Saturation (graph theory); Point (geometry); Mathematics; Econometrics; Linguistics; Computer science; Statistics; History; Combinatorics; Physics; Philosophy; 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":[0.001341455,0.0001017752,0.0001805317,0.000006492719,0.0005110151,0.00009962246,0.0003689861,0.00006790518,0.00006594545],"category_scores_gemma":[0.04763184,0.00004336413,0.00005321392,0.0001701719,0.0005839914,0.00000660851,0.000157707,0.000262324,0.000003415011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007794844,"about_ca_system_score_gemma":0.0000866963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003394126,"about_ca_topic_score_gemma":0.001075037,"domain_scores_codex":[0.998523,0.0007421598,0.0001894223,0.0001678946,0.0002044487,0.0001730851],"domain_scores_gemma":[0.9960065,0.003358443,0.0001080756,0.0002516257,0.000189385,0.00008591876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001168815,0.00001227426,0.003941638,0.00001044858,0.00006164321,0.00001214003,0.7211549,0.000007350225,0.00003915833,0.2588447,0.01523839,0.0005604663],"study_design_scores_gemma":[0.0009889005,0.00003791555,0.0009353096,0.00001922894,0.0001361563,2.927247e-7,0.03417047,0.00007793084,0.000130487,0.0008972664,0.9624959,0.0001101666],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7372012,0.007306634,0.0006438659,0.09498762,0.01690285,0.003507282,0.001014978,0.000232094,0.1382035],"genre_scores_gemma":[0.9811385,0.00006975378,0.00008746068,0.01469489,0.003763963,0.000008927386,0.000006218549,0.000008033848,0.0002222157],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9472575,"threshold_uncertainty_score":0.9603903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009839663554975859,"score_gpt":0.2558818293686235,"score_spread":0.2460421658136476,"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."}}