{"id":"W2591462274","doi":"10.1353/esc.2016.0018","title":"Becoming an “Automatic Gesticulator”: Hollywood’s Mechanized Threat in American Modernist Fiction","year":2016,"lang":"en","type":"article","venue":"English studies in Canada","topic":"Cinema and Media Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hollywood; Art; Literature; Aesthetics; Art history","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":[0.001112878,0.0005013955,0.0001679622,0.0006928632,0.01656977,0.006730177,0.0008443028,0.001665416,0.004982194],"category_scores_gemma":[0.001715469,0.0003238845,0.0001286938,0.0004106305,0.01697604,0.005228616,0.003478262,0.005835724,0.0008883587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003430709,"about_ca_system_score_gemma":0.001069773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01136322,"about_ca_topic_score_gemma":0.03589974,"domain_scores_codex":[0.9991426,0.0004458024,0.00001036416,0.00008428416,0.0001461523,0.00017071],"domain_scores_gemma":[0.9994578,0.0002442271,0.00005418143,0.00004610341,0.00006099542,0.0001366618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006493826,0.00005676496,0.0008859495,0.00004804865,0.000005014846,0.0009085569,0.6637352,0.00005987889,0.0005577583,0.2268352,0.08324993,0.02359282],"study_design_scores_gemma":[0.000006803028,0.00003236499,0.00141797,0.0001201618,0.000006248676,0.0005588035,0.2073203,0.0002227306,0.0004812389,0.01429624,0.7755131,0.00002391838],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.272283,0.008458811,0.004193911,0.08160915,0.002478468,0.00003327805,0.00003348022,0.0001894899,0.6307204],"genre_scores_gemma":[0.8968967,0.002190705,0.0009888776,0.006917285,0.000523093,0.00002805749,0.0000153963,0.0001852754,0.09225453],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01656977,"threshold_uncertainty_score":0.02489167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05024029715350808,"score_gpt":0.256593598292657,"score_spread":0.206353301139149,"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."}}