{"id":"W4389982647","doi":"10.48550/arxiv.2312.10205","title":"Pay to (Not) Play: Monetizing Impatience in Mobile Games","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Digital Platforms and Economics","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Alberta Machine Intelligence Institute; Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency; Canadian Institute for Advanced Research","keywords":"Monetization; Revenue; Gratification; Revenue model; Computer science; Microeconomics; Advertising; Marketing; Business; Economics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002027618,0.001060343,0.0009329186,0.0006569555,0.00148776,0.004860841,0.002156001,0.002110824,0.008482034],"category_scores_gemma":[0.01606822,0.0008199743,0.0007703794,0.0006455468,0.003461683,0.005305351,0.003452462,0.003192262,0.0006577959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003438391,"about_ca_system_score_gemma":0.001344227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005955601,"about_ca_topic_score_gemma":0.007565006,"domain_scores_codex":[0.9977546,0.001224098,0.00006009376,0.000263951,0.0002318796,0.0004653654],"domain_scores_gemma":[0.9929996,0.004331933,0.0008200249,0.0004853643,0.0003432041,0.001020006],"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":[0.0008444443,0.0006278784,0.00809468,0.0002261835,0.000131047,0.0008698609,0.001844695,0.2163185,0.003685779,0.7141848,0.005621123,0.04755098],"study_design_scores_gemma":[0.0001335489,0.0004398164,0.004076925,0.00008753489,0.00009333873,0.0006432331,0.00110911,0.659259,0.0009491558,0.3238834,0.009225258,0.00009964922],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4555951,0.001410084,0.398399,0.007784398,0.0002811805,0.0004033027,0.0002325869,0.0004771056,0.1354173],"genre_scores_gemma":[0.9864899,0.000189999,0.007705287,0.0002443039,0.0000439644,0.00005485937,0.00002255208,0.00004794278,0.005201207],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008482034,"threshold_uncertainty_score":0.02837527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07593730085406457,"score_gpt":0.1766826853866585,"score_spread":0.1007453845325939,"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."}}