{"id":"W2146982527","doi":"10.1287/mnsc.1110.1407","title":"Competing Matchmakers: An Experimental Analysis","year":2011,"lang":"en","type":"article","venue":"Management Science","topic":"Digital Platforms and Economics","field":"Business, Management and Accounting","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada); University of Toronto","funders":"","keywords":"Competition (biology); Market structure; Two-sided market; Computer science; Industrial organization; Economics; Microeconomics; Data science; Network effect; Ecology; Biology","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":[0.005550904,0.0005547807,0.001002379,0.000615574,0.001434828,0.001774962,0.002530422,0.002334877,0.02180036],"category_scores_gemma":[0.0225558,0.0005611933,0.0006820431,0.0007005222,0.001954698,0.001883276,0.001475885,0.00196509,0.001552894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001006241,"about_ca_system_score_gemma":0.001137299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009954936,"about_ca_topic_score_gemma":0.0007393213,"domain_scores_codex":[0.997124,0.001033218,0.0002515304,0.0005543393,0.0006548325,0.0003820154],"domain_scores_gemma":[0.9676977,0.0233588,0.00201547,0.004564182,0.001390078,0.0009737394],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.03371746,0.09703586,0.03869201,0.004174181,0.001233594,0.003390222,0.005088321,0.02152011,0.371314,0.2709204,0.01862568,0.1342881],"study_design_scores_gemma":[0.01496412,0.08760187,0.04956023,0.0004694325,0.001450784,0.003805531,0.004778559,0.2804942,0.166434,0.3386038,0.05119927,0.0006379906],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9602292,0.0004856623,0.0141862,0.000743912,0.0002953423,0.001481058,0.0006578887,0.00009268947,0.02182806],"genre_scores_gemma":[0.9733541,0.0003255879,0.01647574,0.0005053027,0.0001239166,0.001826386,0.0006999605,0.00005428391,0.006634757],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02180036,"threshold_uncertainty_score":0.0729295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0388176845150823,"score_gpt":0.2277099908030616,"score_spread":0.1888923062879793,"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."}}