{"id":"W2439715732","doi":"10.5539/ach.v8n2p83","title":"Chinese Netizens’ Reactions to Red Classics Cinema Animation A Case Study of Taking Tiger Mountain by Strategy (2011)","year":2016,"lang":"en","type":"article","venue":"Asian Culture and History","topic":"Cinema and Media Studies","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"China Scholarship Council","keywords":"Movie theater; Exhibition; Animation; Spectacle; Sociology; Media studies; Opera; China; Aesthetics; Art; Visual arts; History; Political science; Law; Archaeology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001279273,0.0001228143,0.0002803049,0.000110494,0.00009613173,0.000008228115,0.00004807385,0.00006336386,0.0002142983],"category_scores_gemma":[0.00006445319,0.00009416271,0.00003649553,0.00007409102,0.00003814525,0.0001188551,0.00002199923,0.00006700679,0.00003841555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001487682,"about_ca_system_score_gemma":0.00001218545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007087652,"about_ca_topic_score_gemma":0.001345308,"domain_scores_codex":[0.9992249,0.00001290006,0.0003410863,0.0002585509,0.0000314262,0.0001311401],"domain_scores_gemma":[0.9994273,0.00001859836,0.0002613202,0.0001741822,0.00004198293,0.00007666909],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001019037,0.0008418271,0.06495269,0.0001282367,0.000270617,0.0002172089,0.08729809,0.00000358169,0.009902336,0.009788701,0.8125447,0.01395013],"study_design_scores_gemma":[0.00141096,0.0006103253,0.03240334,0.00003077212,0.00002935278,0.00009262122,0.01084023,0.00001106989,0.00001309772,0.0005582373,0.9537055,0.0002945018],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9159997,0.005913326,0.0003133914,0.001604376,0.0004610882,0.0005040388,0.00007510389,0.00004074956,0.07508828],"genre_scores_gemma":[0.9809974,0.0001583302,0.00008377485,0.0000899296,0.00009692503,0.00003200644,0.00000453897,0.00001216824,0.01852497],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1411608,"threshold_uncertainty_score":0.3839844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03025994848501272,"score_gpt":0.2454230826898589,"score_spread":0.2151631342048462,"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."}}