{"id":"W2736154356","doi":"10.3727/152599517x14942648527473","title":"Enabling Event Volunteer Legacies: A Knowledge Management Perspective","year":2017,"lang":"en","type":"article","venue":"Event Management","topic":"Sport and Mega-Event Impacts","field":"Social Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Economic and Social Research Council","keywords":"Human capital; Knowledge management; Knowledge economy; Identification (biology); Capital (architecture); Tourism; Perspective (graphical); Public relations; Sociology; Political science; Business; Economic growth; Geography; Economics; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001552985,0.0002879392,0.0002793462,0.0002659789,0.002246853,0.0005489892,0.001158397,0.00008358657,0.000696101],"category_scores_gemma":[0.00007725714,0.0002872969,0.0002649868,0.0002089468,0.0002431596,0.0006611703,0.0006484109,0.0001664345,0.001039023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009509681,"about_ca_system_score_gemma":0.00007337358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009657822,"about_ca_topic_score_gemma":0.001484652,"domain_scores_codex":[0.997244,0.00009571148,0.0003457673,0.0006029104,0.0008245176,0.0008871185],"domain_scores_gemma":[0.9982426,0.00002502044,0.0002981086,0.0009899351,0.000142959,0.0003014209],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003810028,0.0004824549,0.0005992057,0.0000867948,0.0004061298,0.0001673429,0.01579919,0.00002746853,0.000005084145,0.9227391,0.03005284,0.02959626],"study_design_scores_gemma":[0.0008132952,0.00004367158,0.009411972,0.0001927071,0.0001517649,7.552059e-7,0.02663093,0.00002871933,0.00003027371,0.004356308,0.9579744,0.0003652517],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.02691901,0.0009011053,0.00132608,0.004018787,0.00229647,0.001574628,0.000003971481,0.000200238,0.9627597],"genre_scores_gemma":[0.767862,0.0008482921,0.0003397762,0.0001590286,0.0004453904,0.000122536,0.000004564749,0.00002628971,0.2301921],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9279215,"threshold_uncertainty_score":0.9999579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03742269792724048,"score_gpt":0.3831265856183139,"score_spread":0.3457038876910735,"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."}}