{"id":"W3122096277","doi":"10.1287/mnsc.2020.3683","title":"Joint vs. Separate Crowdsourcing Contests","year":2020,"lang":"en","type":"article","venue":"Management Science","topic":"Experimental Behavioral Economics Studies","field":"Social Sciences","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"CONTEST; Crowdsourcing; Incentive; Randomness; Joint (building); Computer science; Microeconomics; Operations research; Economics; Mathematics; Statistics; Engineering; World Wide Web","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":[0.01157909,0.001716464,0.003288017,0.001023638,0.002122155,0.005533427,0.003058648,0.003883468,0.01712915],"category_scores_gemma":[0.03298119,0.0008937313,0.001933058,0.001092807,0.004586029,0.006644077,0.005043141,0.003218589,0.001062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002747457,"about_ca_system_score_gemma":0.002598071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00151095,"about_ca_topic_score_gemma":0.002545854,"domain_scores_codex":[0.9834035,0.007607513,0.0006758522,0.002516036,0.00233569,0.003461413],"domain_scores_gemma":[0.966413,0.01655154,0.006473343,0.005063205,0.001233124,0.004265651],"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":[0.003975746,0.00160752,0.006098045,0.0008462042,0.0003834028,0.0005047286,0.001240501,0.2059918,0.008399623,0.6974246,0.006821889,0.06670596],"study_design_scores_gemma":[0.0009762754,0.001456216,0.007725516,0.0001689801,0.0002128209,0.0003255462,0.00132909,0.3893502,0.002652504,0.5866252,0.008937707,0.0002400808],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6541524,0.0008937442,0.2513815,0.00291611,0.0004514584,0.001212256,0.0006276338,0.0002569798,0.08810797],"genre_scores_gemma":[0.9807383,0.0001141909,0.01219151,0.0001423548,0.00009837197,0.0003089441,0.00007205296,0.00002985684,0.006304443],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01712915,"threshold_uncertainty_score":0.06123686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07375452277205599,"score_gpt":0.3403048771253577,"score_spread":0.2665503543533017,"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."}}