{"id":"W2972128553","doi":"10.22215/etd/2019-13484","title":"Persuasive Content Generator The Design, Development and Validation of Persuasive Contect Generator Based on Social Media Profiles","year":2019,"lang":"en","type":"dissertation","venue":"","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Popularity; Categorization; Social media; Comprehension; Internet privacy; Persuasion; World Wide Web; The Internet; Generator (circuit theory); Psychology; Social psychology; Power (physics); Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004809327,0.0004234882,0.0005693944,0.000297058,0.000270561,0.0001372477,0.0007282124,0.0002624172,0.0000242495],"category_scores_gemma":[0.0002315506,0.000291124,0.0001615459,0.0003036375,0.00007024795,0.0002133669,0.00006887946,0.0002458399,0.00001002272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001830869,"about_ca_system_score_gemma":0.0006402261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001257108,"about_ca_topic_score_gemma":0.00004075489,"domain_scores_codex":[0.9974921,0.0002725451,0.0005497647,0.0006903387,0.0007372987,0.0002579136],"domain_scores_gemma":[0.9974989,0.0005084772,0.0006644541,0.0004492522,0.0008156581,0.00006330398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001002214,0.0008153733,0.000406539,0.0007428048,0.001653092,0.00003606515,0.09037962,0.0007323693,0.5930313,0.06447019,0.005752718,0.2409777],"study_design_scores_gemma":[0.00044239,0.0001244546,0.0005051177,0.0000768507,0.00007326125,6.635335e-7,0.002852075,0.008730026,0.9864824,0.0001200728,0.0001815979,0.0004111083],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.190315,0.0003468312,0.8058594,0.0002732276,0.00047925,0.001939598,0.0000195468,0.000235012,0.0005320471],"genre_scores_gemma":[0.8321038,0.00003872813,0.1654807,0.0003481065,0.0001384121,0.0004784476,0.0004339247,0.00005473192,0.0009230927],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6417888,"threshold_uncertainty_score":0.9999541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06364569148738479,"score_gpt":0.2915932074818623,"score_spread":0.2279475159944775,"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."}}