{"id":"W3124684686","doi":"","title":"Does Online Search Crowd Out Traditional Search and Improve Matching Efficiency? Evidence from Craigslist*","year":2008,"lang":"en","type":"preprint","venue":"","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Booth University College; University of Toronto","funders":"","keywords":"Renting; Matching (statistics); Search cost; Apartment; Newspaper; Unemployment; Business; Process (computing); Marketing; Advertising; Labour economics; Economics; Computer science; Microeconomics; Political science; Economic growth","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":[0.005347137,0.0003581466,0.0009197013,0.001354711,0.0006091488,0.001533663,0.001059125,0.001442244,0.01624731],"category_scores_gemma":[0.03076086,0.0004011509,0.000783706,0.001868322,0.001510434,0.002620197,0.001232676,0.0008344142,0.001760455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000729046,"about_ca_system_score_gemma":0.0006589536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004034171,"about_ca_topic_score_gemma":0.00347933,"domain_scores_codex":[0.9977775,0.00122343,0.000121937,0.000247469,0.000381995,0.0002476008],"domain_scores_gemma":[0.9034793,0.06869749,0.01618076,0.00795711,0.001607981,0.002077208],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.01530917,0.005902172,0.3666721,0.002388052,0.001671168,0.0003856327,0.00115192,0.01533084,0.00310982,0.0434575,0.02458109,0.5200405],"study_design_scores_gemma":[0.003492491,0.005862981,0.795637,0.0007032622,0.002393065,0.0005977275,0.001386858,0.04146103,0.007714123,0.09782997,0.0427536,0.0001678718],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9100075,0.01468703,0.008231258,0.00761877,0.00009061018,0.0001363439,0.000669403,0.0002479031,0.05831113],"genre_scores_gemma":[0.9920022,0.001513916,0.001643671,0.001168835,0.0001304654,0.00002699743,0.0001715467,0.000033832,0.003308573],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01624731,"threshold_uncertainty_score":0.05435264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09232937969932783,"score_gpt":0.3020147132504548,"score_spread":0.209685333551127,"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."}}