{"id":"W4408155442","doi":"10.1007/s40319-025-01569-6","title":"Control and Compensation. A Comparative Analysis of Copyright Exceptions for Training Generative AI","year":2025,"lang":"en","type":"article","venue":"GRURRR. Gewerblicher Rechtsschutz und Urheberrecht, Rechtsprechungs-Report/GRUR-DVD/GRUR-CD/IIC/Gewerblicher Rechtsschutz und Urheberrecht/Gewerblicher Rechtsschutz und Urheberrecht. Internationaler Teil","topic":"European and International Contract Law","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Humboldt-Universität zu Berlin","keywords":"Generative grammar; Control (management); Compensation (psychology); Training (meteorology); Computer science; Artificial intelligence; Natural language processing; Psychology; Geography; Social psychology","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":["metaresearch","metaepi_narrow","bibliometrics","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","sts","research_integrity"],"category_scores_codex":[0.02008205,0.006849104,0.01000942,0.01178668,0.00538179,0.004460073,0.008436956,0.006207197,0.004366217],"category_scores_gemma":[0.01243518,0.007210072,0.005788012,0.01823443,0.005631564,0.007327991,0.002002739,0.008791696,0.0005559637],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.007305481,"about_ca_system_score_gemma":0.008259016,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01316284,"about_ca_topic_score_gemma":0.02147862,"domain_scores_codex":[0.9536621,0.008574002,0.01269506,0.009741806,0.008199741,0.007127287],"domain_scores_gemma":[0.9545711,0.01283822,0.009170663,0.006619631,0.0130025,0.003797913],"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":[0.008135695,0.01215224,0.02747186,0.001539905,0.1188958,0.001441847,0.03085818,0.004301603,0.02529895,0.5644417,0.1530215,0.05244079],"study_design_scores_gemma":[0.01473574,0.001254649,0.01031284,0.002432914,0.02077887,0.0003832732,0.004227439,0.01488344,0.008856138,0.02540386,0.8880716,0.008659218],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1690684,0.05875743,0.2316362,0.05569643,0.03197385,0.03722025,0.008963565,0.009615701,0.3970682],"genre_scores_gemma":[0.8002299,0.006178056,0.03642953,0.01153764,0.005283043,0.006692886,0.007953485,0.00175481,0.1239406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7350501,"threshold_uncertainty_score":0.9994138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06591012255615582,"score_gpt":0.4000287663136119,"score_spread":0.3341186437574561,"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."}}