{"id":"W2121187044","doi":"10.5539/ass.v4n4p22","title":"The 2008 Olympic Games Leveraging a “Best Ever” Games to Benefit Beijing","year":2009,"lang":"en","type":"article","venue":"Asian Social Science","topic":"Sport and Mega-Event Impacts","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Beijing; Leverage (statistics); Tourism; Government (linguistics); Context (archaeology); Marketing; Business; Public relations; Political science; Computer science; China; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001824991,0.0001585156,0.0001763452,0.0001209889,0.005580021,0.0006370222,0.001082839,0.00007816611,0.00006454618],"category_scores_gemma":[0.0005129639,0.000129507,0.000101241,0.001939596,0.0009226218,0.0007368249,0.00008281996,0.0001571252,0.0001268961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003729245,"about_ca_system_score_gemma":0.0007366402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007644817,"about_ca_topic_score_gemma":0.0009785622,"domain_scores_codex":[0.9969125,0.00005017153,0.0002241476,0.0004053499,0.001226289,0.001181508],"domain_scores_gemma":[0.9989987,0.00007028924,0.0001156985,0.000214261,0.0001527427,0.0004482852],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001023644,0.00004389602,0.001576296,0.00000100215,0.000004749978,0.000008571883,0.05011031,0.000001473978,0.0008915685,0.1209041,0.003364516,0.8230833],"study_design_scores_gemma":[0.0002482935,0.0001076346,0.2600682,0.00005822757,0.00001992074,0.000003720595,0.02540971,0.000005212287,0.0004585767,0.01088904,0.7022504,0.0004810584],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4648985,0.000249691,0.00006613774,0.02784318,0.0008341875,0.0003484209,0.000002213835,0.0001258774,0.5056317],"genre_scores_gemma":[0.9909441,0.00005938022,0.000165929,0.001681315,0.0008023072,0.000008539007,5.552124e-7,0.000007911104,0.006329943],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8226022,"threshold_uncertainty_score":0.9957146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02700188835088902,"score_gpt":0.3258446657797829,"score_spread":0.2988427774288939,"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."}}