{"id":"W2110822016","doi":"10.1177/0256090920060303","title":"<i>The Big Pictures: Sources of National Competitiveness in the Global Movie Industry</i>","year":2006,"lang":"en","type":"article","venue":"Vikalpa The Journal for Decision Makers","topic":"Cultural Industries and Urban Development","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"China; Film industry; Monopoly; Context (archaeology); Distribution (mathematics); Rivalry; Profit (economics); Government (linguistics); Business; Leverage (statistics); International trade; Economics; Advertising; Market economy; Movie theater; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0004535135,0.0002441625,0.000125353,0.001885507,0.001691812,0.006702397,0.0003166572,0.0004914679,0.006167779],"category_scores_gemma":[0.0006535349,0.00007614915,0.0002073804,0.002522315,0.00279997,0.002611894,0.001186217,0.0006803394,0.000261024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004934798,"about_ca_system_score_gemma":0.003436864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05743931,"about_ca_topic_score_gemma":0.1340757,"domain_scores_codex":[0.9997155,0.00007105829,0.000007005407,0.00002847206,0.00006987595,0.0001080808],"domain_scores_gemma":[0.9996368,0.0001128282,0.0000664355,0.00001729971,0.0001006744,0.00006589285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00007653217,0.00005207099,0.06090428,0.0003866631,0.00003860932,0.001258046,0.0393308,0.0007876207,0.001043757,0.784045,0.04623177,0.06584492],"study_design_scores_gemma":[0.000009762801,0.00006534417,0.1943242,0.0006894671,0.00006634508,0.0006777112,0.1512407,0.00131874,0.001899132,0.03878269,0.6108711,0.00005493806],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.297641,0.01054077,0.00292044,0.02517305,0.000470625,0.0000439043,0.000342566,0.00003284106,0.6628348],"genre_scores_gemma":[0.9683975,0.004961242,0.0007686255,0.001745382,0.0002764625,0.00002072482,0.0001471007,0.00003620773,0.02364673],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05743931,"threshold_uncertainty_score":0.11421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04369630885577772,"score_gpt":0.3364705411641735,"score_spread":0.2927742323083958,"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."}}