{"id":"W4380479480","doi":"10.1145/3585088.3589369","title":"When Children Chat with Machine Translated Text: Problems, Possibilities, Potential","year":2023,"lang":"en","type":"article","venue":"","topic":"Digital Communication and Language","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Conversation; Computer science; Machine translation; Android (operating system); Grammar; Natural language processing; Artificial intelligence; Linguistics","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":[0.007197382,0.0008903914,0.0008543147,0.001395007,0.002706537,0.005938113,0.001366,0.003365593,0.002400075],"category_scores_gemma":[0.06119191,0.0009269626,0.0004526093,0.0009187795,0.00319233,0.005347013,0.004329705,0.00204873,0.001173895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00141105,"about_ca_system_score_gemma":0.001141953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002993573,"about_ca_topic_score_gemma":0.004475377,"domain_scores_codex":[0.9860691,0.007932892,0.0009048328,0.001255496,0.00274252,0.001095142],"domain_scores_gemma":[0.9520487,0.03505587,0.005080401,0.002481626,0.003845846,0.00148757],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0003707674,0.0001229298,0.05734265,0.0006410374,0.00006466591,0.01497632,0.8723903,0.0002804943,0.009046067,0.001417832,0.002538543,0.04080842],"study_design_scores_gemma":[0.00006846104,0.000477457,0.07396816,0.001221481,0.0002350609,0.03174586,0.8320717,0.003062652,0.01478529,0.005262456,0.03681616,0.000285395],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886665,0.0006565315,0.00270829,0.001575295,0.0000750577,0.00005363999,0.00008809883,0.0001817829,0.00599479],"genre_scores_gemma":[0.993741,0.0003330235,0.003234181,0.0003116588,0.0000282078,0.0000874185,0.00009285598,0.00007632343,0.002095326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007197382,"threshold_uncertainty_score":0.03806382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009257764750661144,"score_gpt":0.2106307935236666,"score_spread":0.2013730287730055,"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."}}