{"id":"W2952446056","doi":"10.5539/ijel.v9n4p42","title":"Genre Analysis of Research Article Abstracts in Linguistics and Literature: A Cross Disciplinary Study","year":2019,"lang":"en","type":"article","venue":"International Journal of English Linguistics","topic":"Discourse Analysis in Language Studies","field":"Arts and Humanities","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Macro; Genre analysis; Applied linguistics; Text linguistics; Corpus linguistics; Focus (optics); Discipline; Linguistics; Computer science; Cognitive linguistics; Sociology; Natural language processing; Psychology; Social science; Cognition; Philosophy; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001567811,0.0001213843,0.000412973,0.001159305,0.00006889464,0.0003512087,0.0003968217,0.00004074105,0.0002094903],"category_scores_gemma":[0.05132644,0.0001003815,0.0001488305,0.0003180407,0.0002731959,0.00006520686,0.0001970687,0.0004524458,0.000003280628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006652813,"about_ca_system_score_gemma":0.00006145898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006836135,"about_ca_topic_score_gemma":0.0007554966,"domain_scores_codex":[0.9977549,0.00009761903,0.0008787903,0.0001733375,0.0009111937,0.0001842091],"domain_scores_gemma":[0.9719248,0.000622381,0.000418963,0.00018322,0.02679196,0.00005864355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004046895,0.002020375,0.5860457,0.0000855945,0.006677076,0.0008692101,0.3053509,0.008681395,0.000009038067,0.08888899,0.0007390631,0.0002279649],"study_design_scores_gemma":[0.005614245,0.002313897,0.504683,0.001528218,0.003597067,0.000004388547,0.3698206,0.003665375,0.0001755659,0.0094566,0.0982127,0.00092835],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9176007,0.0009041323,0.000001172256,0.000009243848,0.005025513,0.00008978386,0.00006293835,0.000005984334,0.07630055],"genre_scores_gemma":[0.9926159,0.00006639955,0.0001120286,0.00001433524,0.006593582,0.000001389738,0.000009513766,0.00001237279,0.0005744862],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09747364,"threshold_uncertainty_score":0.9566646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03955540042732672,"score_gpt":0.3950844782743817,"score_spread":0.355529077847055,"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."}}