{"id":"W4376865411","doi":"10.5539/ibr.v16n6p15","title":"Stock Markets and Major Sport Events: Evidence from Cricket World Cup 2019","year":2023,"lang":"en","type":"article","venue":"International Business Research","topic":"Sport and Mega-Event Impacts","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cricket; Stock exchange; Stock (firearms); Stock market; Ordinary least squares; Event study; Financial economics; Economics; Business; Econometrics; Geography; Finance","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001594702,0.0003285345,0.0003791772,0.001838014,0.000949691,0.002021949,0.0006067242,0.0005750768,0.005640288],"category_scores_gemma":[0.008421248,0.0002348862,0.0006662853,0.002731498,0.0006682268,0.001044966,0.001177456,0.001241676,0.0006126165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008561185,"about_ca_system_score_gemma":0.0008763261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03331912,"about_ca_topic_score_gemma":0.05475451,"domain_scores_codex":[0.9989694,0.0002762697,0.0001028971,0.0001425977,0.0003301791,0.0001786184],"domain_scores_gemma":[0.9873235,0.003292998,0.006382692,0.0003379082,0.001309654,0.001353422],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004361797,0.0005548805,0.9829543,0.0002413805,0.0003097325,0.0002388475,0.00171306,0.00005189149,0.00007763263,0.0004204512,0.001844433,0.01115722],"study_design_scores_gemma":[0.000004673106,0.00008299996,0.996621,0.00006691557,0.00004850111,0.00002517167,0.002184664,0.00005176592,0.00003423497,0.00002917868,0.0008454727,0.000005433905],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913224,0.001550957,0.00004269442,0.0008191937,0.00005273963,0.00002801031,0.0009124675,0.000002813173,0.00526874],"genre_scores_gemma":[0.9956148,0.001801636,0.00006082542,0.000195094,0.00008498183,0.00002798106,0.001030662,0.000003588845,0.00118044],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03331912,"threshold_uncertainty_score":0.06625032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1660544119566572,"score_gpt":0.4690701946451019,"score_spread":0.3030157826884446,"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."}}