{"id":"W3004276422","doi":"10.1075/langct.00025.cum","title":"Nominalisation and genre in early discourses on electricity","year":2020,"lang":"en","type":"article","venue":"Language Context and Text The Social Semiotics Forum","topic":"Discourse Analysis in Language Studies","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Argument (complex analysis); Electricity; Key (lock); Field (mathematics); Register (sociolinguistics); Epistemology; State (computer science); Phase (matter); Linguistics; Sociology; History; Computer science; Philosophy; Engineering; Mathematics; Electrical engineering; Physics; Pure mathematics; Chemistry; Algorithm; Quantum mechanics","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":[0.01324242,0.0004894091,0.0007100935,0.004782708,0.008044686,0.01324598,0.001344479,0.002629946,0.006283985],"category_scores_gemma":[0.03274807,0.0003960519,0.0003892091,0.003817073,0.03005615,0.0173402,0.007130082,0.00405963,0.0005716439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0104406,"about_ca_system_score_gemma":0.001719709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004794614,"about_ca_topic_score_gemma":0.002885804,"domain_scores_codex":[0.9801845,0.01518779,0.0006455843,0.00102332,0.002334661,0.0006241944],"domain_scores_gemma":[0.9655034,0.02818479,0.00167643,0.001400026,0.002413346,0.0008220763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00006085528,0.00001291276,0.0007094874,0.00009337717,0.000004304265,0.0002579449,0.4479729,0.00007503746,0.000638115,0.5435845,0.001375535,0.005215081],"study_design_scores_gemma":[0.00004412726,0.00006886926,0.003008704,0.0009715991,0.00001798803,0.000633159,0.437612,0.001334818,0.00173297,0.2772032,0.2772819,0.00009075307],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4567667,0.006211079,0.02576244,0.02880348,0.001287597,0.000103486,0.0002869548,0.0001718321,0.4806064],"genre_scores_gemma":[0.9919653,0.0004912033,0.001172908,0.0003169122,0.0001735382,0.00002318854,0.00004666148,0.00006041077,0.005749867],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01324598,"threshold_uncertainty_score":0.07575226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02163592597495601,"score_gpt":0.268146678397023,"score_spread":0.246510752422067,"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."}}