{"id":"W7160242492","doi":"10.69513/ilcr.v2.i1.a5","title":"Detecting Censorship and Self-Censorship: NLP Analysis of Political Discourse in Iraqi Social Media and Blogs","year":2024,"lang":"","type":"article","venue":"Iraqi Literary and Cultural Review (ILCR)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Regional Municipality of Niagara","funders":"","keywords":"Politics; Censorship; Taboo; Social media; Harm; Reading (process); Criticism; Critical discourse analysis; Key (lock)","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":[0.001538511,0.000630411,0.001420674,0.0005043671,0.0003581445,0.0008478589,0.0003501195,0.0003231693,0.00006902276],"category_scores_gemma":[0.000381764,0.0004933584,0.0004088057,0.002950069,0.0004442217,0.001563756,0.0003476001,0.0008528186,0.000009911709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007857877,"about_ca_system_score_gemma":0.0000812397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006916248,"about_ca_topic_score_gemma":0.0000705059,"domain_scores_codex":[0.9949971,0.0009384135,0.001285084,0.00129787,0.0005301124,0.0009514433],"domain_scores_gemma":[0.9980643,0.0006790629,0.0002486071,0.0003613052,0.0001425027,0.0005042379],"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":[0.0001049912,0.000367915,0.005780718,0.01762657,0.001632649,0.0005997376,0.05569641,0.000001722561,0.0009106568,0.07447094,0.0001819688,0.8426257],"study_design_scores_gemma":[0.007507606,0.003896846,0.5188336,0.1131737,0.04340489,0.005718418,0.01154393,0.1627474,0.003975241,0.04418595,0.07153241,0.01348004],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7013701,0.2948901,0.00007763607,0.002407203,0.0003859621,0.0004151156,0.00006520448,0.0001090347,0.0002795793],"genre_scores_gemma":[0.9476579,0.05067762,0.0006081316,0.0005004716,0.0003498945,0.00002282434,0.00002602117,0.00002343845,0.0001336718],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8291457,"threshold_uncertainty_score":0.9997518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02466735791376496,"score_gpt":0.3040283281900425,"score_spread":0.2793609702762775,"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."}}