{"id":"W2330287116","doi":"10.14288/1.0072728","title":"Metaphors for thinking in modern Mandarin Chinese : a corpus study","year":2012,"lang":"en","type":"article","venue":"Open Collections","topic":"Language, Metaphor, and Cognition","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Mandarin Chinese; Linguistics; Metaphor; Natural language processing; Computer science; Artificial intelligence; Philosophy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001525096,0.0004255514,0.0004435357,0.003309905,0.003468191,0.001189248,0.0005854314,0.0005060001,0.003806692],"category_scores_gemma":[0.003800513,0.0002170904,0.0002594965,0.00581817,0.002218597,0.001439534,0.001418917,0.0008370908,0.0003195854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002544655,"about_ca_system_score_gemma":0.001861942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0340299,"about_ca_topic_score_gemma":0.05753132,"domain_scores_codex":[0.9993056,0.0002918239,0.00007786384,0.0001164924,0.0001376151,0.00007061904],"domain_scores_gemma":[0.9975873,0.001565126,0.0002349936,0.0002076816,0.0003048218,0.0001001197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0004204192,0.0004701624,0.06874954,0.00330819,0.0001014689,0.008564074,0.7143906,0.0005225611,0.02409202,0.0237179,0.01602063,0.1396423],"study_design_scores_gemma":[0.00008591182,0.0002468636,0.4451253,0.0005466403,0.0001671844,0.005722033,0.3218414,0.002545999,0.007977609,0.002087255,0.2134982,0.0001555558],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9858106,0.001579213,0.001230317,0.0004035729,0.00003780539,0.000145001,0.001295677,0.00002159156,0.009476104],"genre_scores_gemma":[0.9885796,0.001552226,0.003483419,0.0001116052,0.00004258523,0.0003385308,0.002490358,0.0000361514,0.003365481],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0340299,"threshold_uncertainty_score":0.06766367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03597061327884934,"score_gpt":0.3484475899157717,"score_spread":0.3124769766369223,"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."}}