{"id":"W4412480212","doi":"10.16995/intransition.17147","title":"“Animation in London/Matchmove in Bangalore”: Territorial Profiles of Visual Effects (VFX) Workforces in the Global Media Industries","year":2025,"lang":"en","type":"article","venue":"[in]Transition","topic":"Intellectual Property Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001377774,0.0001268422,0.0002490971,0.0002764676,0.00005949038,0.00005305244,0.000214733,0.0002871441,0.00002349508],"category_scores_gemma":[0.000585393,0.0001055417,0.00003496106,0.001791023,0.0002091964,0.0003818665,0.00001610281,0.0003369442,0.000005229533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000494785,"about_ca_system_score_gemma":0.0002395906,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.03330036,"about_ca_topic_score_gemma":0.2293683,"domain_scores_codex":[0.9977485,0.0008871556,0.0004305815,0.0002182661,0.0004010818,0.000314436],"domain_scores_gemma":[0.9992494,0.0005469226,0.00005899052,0.00008299176,0.00004248117,0.00001924428],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.002099728,0.001645455,0.1333418,0.000581636,0.0000252225,0.0001239599,0.7523884,0.002188282,0.00401425,0.08058096,0.001053665,0.0219567],"study_design_scores_gemma":[0.01435605,0.00104214,0.6692915,0.009724711,0.00007350438,0.000003699284,0.1708187,0.009308489,0.04102455,0.08077917,0.002032728,0.001544808],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903045,0.00009080717,0.00006079029,0.00166201,0.0006411001,0.000865252,0.000007447152,0.00001767064,0.006350411],"genre_scores_gemma":[0.9994637,0.00003071213,0.00002537353,0.0001341003,0.0002266177,0.00007902055,0.00002393705,0.000005027466,0.00001147923],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5815697,"threshold_uncertainty_score":0.973137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01355389342926011,"score_gpt":0.2933069572844257,"score_spread":0.2797530638551656,"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."}}