{"id":"W3122446134","doi":"10.1257/mac.20180461","title":"Advertising, Innovation, and Economic Growth","year":2021,"lang":"en","type":"article","venue":"American Economic Journal Macroeconomics","topic":"Firm Innovation and Growth","field":"Economics, Econometrics and Finance","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Microfoundations; Economics; Growth model; Channel (broadcasting); Econometrics; Empirical evidence; Advertising; Microeconomics; Business; Telecommunications; Computer science; Macroeconomics","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","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006286163,0.0003372163,0.0008265653,0.0006521527,0.0003144728,0.0005149557,0.0003340292,0.0001009781,0.001919306],"category_scores_gemma":[0.0001288319,0.0004590852,0.0001567878,0.0002858225,0.0003636388,0.0008658784,0.0001635017,0.000440245,0.001812256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008621727,"about_ca_system_score_gemma":0.0003091822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003102853,"about_ca_topic_score_gemma":0.0001256492,"domain_scores_codex":[0.9968017,0.00003328879,0.001869048,0.0007192558,0.00002332385,0.0005533496],"domain_scores_gemma":[0.9975827,0.00008782824,0.001605146,0.000410267,0.000103258,0.00021081],"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":[0.00002212448,0.00003355078,0.1982439,0.0000118878,0.0001876062,0.00001926615,0.0001979574,0.0001238446,0.00003419648,0.7875552,0.006026884,0.007543629],"study_design_scores_gemma":[0.003552375,0.0002831536,0.169594,0.00003625419,0.00002871909,0.00265402,0.001079359,0.004966699,0.001307622,0.3821955,0.4322619,0.002040402],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9581168,0.0008958901,0.001730968,0.006318004,0.001603871,0.0001131963,0.0002546659,0.0000585844,0.03090804],"genre_scores_gemma":[0.9869913,0.001936697,0.003995181,0.004919132,0.0006824352,0.00001105367,0.000035967,0.00008647342,0.001341686],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.426235,"threshold_uncertainty_score":0.9997861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0120281364135401,"score_gpt":0.2262665414716945,"score_spread":0.2142384050581544,"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."}}