{"id":"W2324029037","doi":"10.1177/1527476415577211","title":"Scenes from an Imaginary Country","year":2015,"lang":"en","type":"article","venue":"Television & New Media","topic":"Cinema and Media Studies","field":"Economics, Econometrics and Finance","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Research England; Social Sciences and Humanities Research Council of Canada; Microsoft Research","keywords":"NTSC; The Imaginary; Perception; Test (biology); Representation (politics); Computer science; Politics; Sociology; Telecommunications; Psychology; Law; Political science; High-definition television","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003843452,0.0003484129,0.0001550402,0.0007598216,0.002746448,0.00460985,0.0003422672,0.0006015936,0.02709474],"category_scores_gemma":[0.001669864,0.0001558527,0.0002913934,0.0009169648,0.002187498,0.001901201,0.001633717,0.001765425,0.002121302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001259325,"about_ca_system_score_gemma":0.0003852845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006306265,"about_ca_topic_score_gemma":0.0155019,"domain_scores_codex":[0.9997024,0.0001619333,0.000005469426,0.00003563831,0.00004788072,0.00004665409],"domain_scores_gemma":[0.9995895,0.0001935267,0.00002583975,0.00006412617,0.00005039046,0.00007663123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004633769,0.0001289447,0.00330074,0.0003514181,0.00004193834,0.001538153,0.04802812,0.0009915381,0.002345445,0.5232484,0.356403,0.06315894],"study_design_scores_gemma":[0.00001794104,0.0000619496,0.004647873,0.000147407,0.00001296565,0.0005456508,0.01846284,0.0004655836,0.0005934428,0.01476474,0.9602541,0.00002552063],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1684646,0.003063793,0.00688637,0.0161672,0.002762282,0.00008662524,0.003023912,0.0002989088,0.7992464],"genre_scores_gemma":[0.9006009,0.001845425,0.004078796,0.002390215,0.0007453454,0.00004676231,0.001819235,0.0003050656,0.08816832],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02709474,"threshold_uncertainty_score":0.09064096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06541921989701711,"score_gpt":0.2664138120524865,"score_spread":0.2009945921554694,"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."}}