{"id":"W4415383338","doi":"10.5430/wjel.v16n2p143","title":"A Corpus-based Re-categorization for English and Chinese Hedges","year":2025,"lang":"","type":"article","venue":"World Journal of English Language","topic":"E-commerce and Technology Innovations","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Categorization; Corpus linguistics; Operationalization; Pragmatics; Contrastive analysis; Contrastive linguistics; Phenomenon; Coherence (philosophical gambling strategy); Hedge","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":[0.00435454,0.0004266894,0.0004382605,0.01006008,0.00286606,0.0018175,0.0008920867,0.0005811168,0.00238335],"category_scores_gemma":[0.01217622,0.0003093309,0.0005158264,0.007361469,0.001469458,0.003503767,0.002566109,0.0008503339,0.0005686295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002465344,"about_ca_system_score_gemma":0.002645426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01657127,"about_ca_topic_score_gemma":0.0264833,"domain_scores_codex":[0.9970018,0.0008774275,0.000528986,0.0009207708,0.0005334945,0.0001374437],"domain_scores_gemma":[0.9886889,0.003709988,0.0008814356,0.002544127,0.003925375,0.0002501963],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005406084,0.0004511992,0.09366418,0.003112335,0.000128521,0.002093419,0.2385792,0.0009937527,0.09673284,0.0421881,0.03424796,0.4872678],"study_design_scores_gemma":[0.0001277305,0.0002438118,0.385348,0.001551918,0.0003426232,0.001750029,0.1640143,0.01635479,0.03764438,0.008256013,0.3840483,0.0003183025],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8957024,0.002031463,0.05507628,0.000864034,0.0003483666,0.00300347,0.01429186,0.0005400465,0.02814201],"genre_scores_gemma":[0.7867396,0.0008215905,0.1676364,0.0002442838,0.00007665961,0.005680239,0.03283009,0.0002351542,0.005735899],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01657127,"threshold_uncertainty_score":0.03294957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008252946676219378,"score_gpt":0.2537107528149841,"score_spread":0.2454578061387648,"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."}}