{"id":"W7116982804","doi":"10.18254/s207751800037159-3","title":"Artificial Intelligence in Creative Industries: Demand Generation in a New Segment of the Global Technology Market","year":2025,"lang":"en","type":"article","venue":"Artificial Societies","topic":"Cultural Industries and Urban Development","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Demand forecasting; Product (mathematics); Supply and demand; New product development; Market segmentation; Production (economics)","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0005110325,0.0001442755,0.0001686029,0.0009223041,0.001068287,0.003667901,0.0003424485,0.001426227,0.0078673],"category_scores_gemma":[0.00149172,0.0001230101,0.0002515705,0.002266569,0.001184168,0.004254902,0.001484501,0.0008554657,0.0007065584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001551941,"about_ca_system_score_gemma":0.0008880448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002481273,"about_ca_topic_score_gemma":0.004593068,"domain_scores_codex":[0.9996021,0.0001022321,0.00001361037,0.00005056391,0.0001304978,0.0001009781],"domain_scores_gemma":[0.9990804,0.0003542091,0.0001503174,0.00004242031,0.0001429435,0.0002297143],"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":[0.0005719473,0.0005820011,0.2055174,0.0006297285,0.00004681783,0.003591788,0.04209949,0.00325601,0.009334055,0.3635451,0.04102449,0.3298012],"study_design_scores_gemma":[0.00009862211,0.0003378898,0.4559913,0.0003979868,0.00005284407,0.002677627,0.1220292,0.01824012,0.003185442,0.161643,0.2351785,0.0001675836],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8092806,0.002197477,0.003152753,0.02160205,0.000122123,0.00004351508,0.0003678986,0.00003750183,0.1631961],"genre_scores_gemma":[0.9916878,0.001148204,0.000544256,0.0006523895,0.0001225426,0.00001441062,0.0001201282,0.00001120737,0.00569891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0078673,"threshold_uncertainty_score":0.02631879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05676776187034394,"score_gpt":0.3198566950416655,"score_spread":0.2630889331713215,"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."}}