{"id":"W3041002373","doi":"10.1007/978-3-030-50341-3_23","title":"Investigating Linguistic Indicators of Generative Content in Enterprise Social Media","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Knowledge Management and Sharing","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Generative grammar; Computer science; Generative model; Affordance; Social media; Field (mathematics); Knowledge management; Sample (material); Analytics; Data science; Artificial intelligence; Human–computer interaction; World Wide Web","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":[0.001527252,0.0002302857,0.0001825738,0.003509488,0.0006318198,0.003863461,0.0004226658,0.0006606496,0.002866611],"category_scores_gemma":[0.01884006,0.000263781,0.0002062533,0.002707851,0.0007835396,0.002891908,0.001367534,0.0007148004,0.0007136781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005937189,"about_ca_system_score_gemma":0.0002984594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0028272,"about_ca_topic_score_gemma":0.002729844,"domain_scores_codex":[0.9989105,0.0005202983,0.00007681573,0.0001449899,0.0002537044,0.00009358674],"domain_scores_gemma":[0.9769818,0.0184513,0.002022266,0.0005139158,0.001642411,0.0003882727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002213214,0.0008048113,0.6273493,0.0007657221,0.0002899517,0.0005941365,0.05185331,0.004554512,0.113167,0.0129259,0.001967093,0.183515],"study_design_scores_gemma":[0.00003811974,0.0006297552,0.8647067,0.0001926355,0.0002506599,0.0005260016,0.03797553,0.04788694,0.03332342,0.01021468,0.004120202,0.000135343],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910738,0.00009229377,0.003817325,0.00005797455,0.000008284343,0.00002344417,0.000268057,0.00006646295,0.00459248],"genre_scores_gemma":[0.9972832,0.00003884144,0.001819557,0.00001105412,0.00001199505,0.00003080912,0.0002231305,0.00002883199,0.0005525594],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003863461,"threshold_uncertainty_score":0.009589732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05510611795529285,"score_gpt":0.2892827859436206,"score_spread":0.2341766679883277,"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."}}