{"id":"W2259733669","doi":"","title":"The Use of SMS and Language Transformation in Bangladesh","year":2010,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Digital Communication and Language","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Hindi; Bengali; Short Message Service; Capitalization; Typing; Computer science; Advertising; Psychology; Linguistics; Business; Artificial intelligence; Speech recognition; Telecommunications","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":[0.000645107,0.0002080068,0.0001371128,0.001272774,0.0009231367,0.001559467,0.0002874723,0.0003831539,0.004076241],"category_scores_gemma":[0.004080379,0.0001718833,0.0001072635,0.002187066,0.001027063,0.001193714,0.001039516,0.0005396177,0.0008943284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001292915,"about_ca_system_score_gemma":0.0007037601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01505542,"about_ca_topic_score_gemma":0.01570733,"domain_scores_codex":[0.9987749,0.0005976927,0.00009807297,0.0001087177,0.0002264296,0.0001940457],"domain_scores_gemma":[0.9974808,0.0009225148,0.0008337908,0.00008011879,0.0003816932,0.0003011148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003362477,0.00009783472,0.7631761,0.000262365,0.00003066153,0.002954778,0.1348078,0.0002270637,0.007924299,0.002378336,0.0009643286,0.08683998],"study_design_scores_gemma":[0.00001030225,0.0002391752,0.803767,0.000150012,0.0000206913,0.002344125,0.1758771,0.0003211338,0.001538932,0.0005776066,0.01509088,0.00006293562],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895368,0.0004330357,0.0000973884,0.0005609654,0.000006640916,0.00001176648,0.0001373689,0.00000629447,0.009209766],"genre_scores_gemma":[0.9987141,0.000374124,0.00005008915,0.00002821371,0.000003502268,0.000005600265,0.00002715054,0.000002436549,0.000794822],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01505542,"threshold_uncertainty_score":0.0299356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01042061342777978,"score_gpt":0.2360736357065746,"score_spread":0.2256530222787949,"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."}}