{"id":"W4247185362","doi":"10.31234/osf.io/b9puq","title":"Chaining and the growth of linguistic categories","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Language, Metaphor, and Cognition","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Alberta","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Numeral system; Categorization; Linguistics; Chaining; Noun; Context (archaeology); Natural language processing; Cognitive linguistics; Computer science; Cognition; Artificial intelligence; Psychology; History","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003264503,0.0002734123,0.000366836,0.002160528,0.001698014,0.003031901,0.001012248,0.001003477,0.004806976],"category_scores_gemma":[0.03120432,0.0003126355,0.0006860263,0.002295239,0.005504206,0.01102938,0.003551822,0.001241771,0.000586729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001355233,"about_ca_system_score_gemma":0.0006450179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003548246,"about_ca_topic_score_gemma":0.003153754,"domain_scores_codex":[0.9985484,0.0005381432,0.00006492449,0.0004315862,0.0002645714,0.0001523357],"domain_scores_gemma":[0.9783558,0.0121575,0.002813167,0.004065448,0.001832779,0.0007752678],"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":[0.0002991933,0.000115531,0.0813913,0.0003557389,0.00007667904,0.001382013,0.04514471,0.0171343,0.01321934,0.5958959,0.001232195,0.2437531],"study_design_scores_gemma":[0.00002035365,0.0001301421,0.03240192,0.0001327553,0.00004822769,0.001212361,0.007095775,0.04877076,0.003665108,0.8868553,0.0195827,0.000084593],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8889115,0.001008411,0.08428019,0.0009275238,0.00004821087,0.00004552619,0.0001315895,0.0002349618,0.02441214],"genre_scores_gemma":[0.981047,0.000247355,0.0171024,0.00004810567,0.00001261826,0.00002855543,0.00009336526,0.00003598498,0.001384678],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004806976,"threshold_uncertainty_score":0.01726454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03220520850461038,"score_gpt":0.2994077070511202,"score_spread":0.2672024985465098,"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."}}