{"id":"W2169593491","doi":"10.1287/mnsc.1110.1322","title":"Incentives and Problem Uncertainty in Innovation Contests: An Empirical Analysis","year":2011,"lang":"en","type":"article","venue":"Management Science","topic":"Experimental Behavioral Economics Studies","field":"Social Sciences","cited_by":839,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Competitor analysis; CONTEST; Rivalry; Incentive; Value (mathematics); Microeconomics; Economics; Industrial organization; Set (abstract data type); Competition (biology); Marketing; Business; Computer science; Political science","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":[0.01870603,0.0006377347,0.001311099,0.002774677,0.001435771,0.003553061,0.002002664,0.001913881,0.008887134],"category_scores_gemma":[0.137822,0.0006770912,0.001479198,0.003781667,0.002223159,0.003019347,0.00235109,0.003542497,0.0007392936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001821152,"about_ca_system_score_gemma":0.001400093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003756125,"about_ca_topic_score_gemma":0.002948391,"domain_scores_codex":[0.9886856,0.006930423,0.0006822352,0.0007601809,0.001778573,0.001163136],"domain_scores_gemma":[0.589574,0.3498329,0.0430127,0.007364478,0.004395684,0.005820287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002879471,0.006174879,0.8756105,0.0005959462,0.0007100155,0.0006522656,0.002448658,0.03612151,0.0006525525,0.02034034,0.004533886,0.04927996],"study_design_scores_gemma":[0.0007453902,0.001957161,0.7585418,0.0001969369,0.000328318,0.0008905283,0.004402347,0.1981305,0.0005313491,0.02764526,0.006471398,0.0001590233],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935674,0.0005244144,0.002183201,0.0003427952,0.00001127361,0.0001665125,0.000252478,0.00001669713,0.002935268],"genre_scores_gemma":[0.998235,0.0001538766,0.0007049244,0.00004311021,0.00002546107,0.00008805947,0.0002819968,0.000009830318,0.0004576218],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01870603,"threshold_uncertainty_score":0.09892809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09206989126865556,"score_gpt":0.3864339005795512,"score_spread":0.2943640093108956,"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."}}