{"id":"W2693240795","doi":"10.5430/ijhe.v6n3p231","title":"Morphological Adaptation of English Loanwords in Twitter: Educational Implications","year":2017,"lang":"en","type":"article","venue":"International Journal of Higher Education","topic":"Linguistics, Language Diversity, and Identity","field":"Arts and Humanities","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Adaptation (eye); Interview; Affect (linguistics); Computer science; Loan; Linguistics; Sociology; Psychology; Business","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004308319,0.0002114357,0.0001255371,0.0006781281,0.0014552,0.001353407,0.000227925,0.0003346954,0.004952216],"category_scores_gemma":[0.00223059,0.0001374592,0.0001075949,0.0007578477,0.001291575,0.001752021,0.001524189,0.0004710264,0.0006575302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008311274,"about_ca_system_score_gemma":0.000638407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009807872,"about_ca_topic_score_gemma":0.01794402,"domain_scores_codex":[0.9996301,0.0001529521,0.00003134835,0.00005268845,0.00005804397,0.00007488974],"domain_scores_gemma":[0.9989214,0.0003710506,0.0002731473,0.00008426252,0.0002336342,0.0001165203],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004543719,0.0003849315,0.4989441,0.000297559,0.00001976397,0.004972183,0.3602921,0.0002118716,0.02914418,0.003424871,0.001389693,0.1004644],"study_design_scores_gemma":[0.000004386424,0.00007997503,0.666816,0.00008825034,0.00001822361,0.0007331622,0.3203714,0.0003012268,0.003585424,0.0009765846,0.006996807,0.00002866638],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955112,0.0000588199,0.00008555381,0.0002771196,0.000004803609,0.000006272669,0.00003225832,0.000004323873,0.004019766],"genre_scores_gemma":[0.9986984,0.00008709064,0.0001019034,0.00003860167,0.000003203047,0.000005924303,0.0000203381,0.000003703991,0.001040761],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009807872,"threshold_uncertainty_score":0.01950157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05358486332950122,"score_gpt":0.3302813569368386,"score_spread":0.2766964936073374,"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."}}