{"id":"W2072602489","doi":"10.1287/mksc.1100.0606","title":"Preview Provision Under Competition","year":2010,"lang":"en","type":"article","venue":"Marketing Science","topic":"Media Influence and Politics","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Competition (biology); Newspaper; Incentive; Context (archaeology); Product (mathematics); Business; Advertising; Product differentiation; Industrial organization; Marketing; Control (management); Computer science; Microeconomics; Economics; Artificial intelligence","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":[0.004320495,0.0008865453,0.001118444,0.001035631,0.001793542,0.00455292,0.001046821,0.002622046,0.02621293],"category_scores_gemma":[0.02073492,0.00057442,0.0007540596,0.0008980009,0.002901038,0.005078704,0.003302575,0.002349099,0.001588766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003010102,"about_ca_system_score_gemma":0.002587947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004375136,"about_ca_topic_score_gemma":0.003736625,"domain_scores_codex":[0.9938897,0.001577956,0.0002606456,0.001192476,0.001144483,0.001934736],"domain_scores_gemma":[0.9754364,0.01017314,0.006420072,0.003017332,0.002385732,0.002567293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0019609,0.001285216,0.05140289,0.0006485836,0.0001763649,0.001760253,0.001815362,0.03221547,0.01536995,0.7797049,0.007990792,0.1056694],"study_design_scores_gemma":[0.0009390314,0.003744044,0.09621328,0.0002901452,0.000537397,0.002617379,0.004531268,0.1055424,0.01418474,0.7023128,0.06867054,0.0004168828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7135581,0.0009849312,0.04920642,0.003206953,0.0001099221,0.0003830317,0.0005823391,0.0003197497,0.2316486],"genre_scores_gemma":[0.9936021,0.00009101722,0.001493118,0.0001929403,0.00006332865,0.00004913501,0.00004817041,0.00001579915,0.004444282],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02621293,"threshold_uncertainty_score":0.08769101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01929770103109325,"score_gpt":0.3535575933496526,"score_spread":0.3342598923185593,"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."}}