{"id":"W4417170276","doi":"10.1109/mc.2025.3614083","title":"Agentic Data as Muse, Strategist, and Composer in Entrepreneurial Creativity","year":2025,"lang":"","type":"article","venue":"Computer","topic":"Innovation, Sustainability, Human-Machine Systems","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Royal University","funders":"","keywords":"Creativity; Generative grammar; Data collection; Entrepreneurship; Generative model","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002795942,0.0003514363,0.0005848769,0.0003926427,0.0006081586,0.0008048018,0.001243002,0.0002395982,0.0002785473],"category_scores_gemma":[0.0003026599,0.0003805695,0.00006488974,0.001452504,0.0007940137,0.0008162676,0.001258675,0.0004301424,0.0000308258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002886025,"about_ca_system_score_gemma":0.0008931856,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01375858,"about_ca_topic_score_gemma":0.008282329,"domain_scores_codex":[0.9950951,0.001592952,0.0009427452,0.001274594,0.0004760545,0.0006185776],"domain_scores_gemma":[0.9972461,0.0006159215,0.0002613209,0.001431872,0.0003329729,0.0001118132],"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":[0.0002007346,0.001564371,0.4391194,0.001355485,0.0002634012,0.0001564602,0.03871447,0.0006698644,0.00004434375,0.4789791,0.01706239,0.02186996],"study_design_scores_gemma":[0.005779324,0.0004397359,0.6121674,0.0009548358,0.000217124,0.00001102369,0.006632086,0.1223155,0.0000355923,0.09998534,0.1498062,0.001655859],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9568827,0.0004864787,0.00792384,0.003089495,0.004793167,0.001767418,0.00004369463,0.00008579335,0.0249274],"genre_scores_gemma":[0.9965833,0.00006030482,0.000360155,0.000327436,0.0009162673,0.00001292527,0.00008651033,0.00001567796,0.001637398],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3789938,"threshold_uncertainty_score":0.9998646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04369071123409199,"score_gpt":0.3718705448015795,"score_spread":0.3281798335674875,"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."}}