{"id":"W2171824286","doi":"10.1142/s1363919614500376","title":"INCORPORATING NETWORK ANALYSIS INTO EVALUATION OF 'BIG SCIENCE' PROJECTS: AN ASSESSMENT OF THE CANADIAN LIGHT SOURCE SYNCHROTRON","year":2014,"lang":"en","type":"article","venue":"International Journal of Innovation Management","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Secretaría de Ciencia y Técnica, Universidad de Buenos Aires; Genome Prairie; Genome Canada; Canadian Light Source","keywords":"CLs upper limits; Social network analysis; Government (linguistics); Big data; Scale (ratio); Scientific instrument; Network science; Work (physics); Data science; Core (optical fiber); Computer science; Public relations; Sociology; Political science; Social media; Telecommunications; Engineering; Complex network; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":["metaresearch","bibliometrics"],"category_scores_codex":[0.1106604,0.000095514,0.0002523437,0.05603551,0.0002825427,0.0009209047,0.003278989,0.00004264543,0.00007997071],"category_scores_gemma":[0.01188844,0.00006136722,0.0001197179,0.1543228,0.0002270803,0.000723946,0.0004380498,0.0001814909,0.000001521606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001106619,"about_ca_system_score_gemma":0.001834536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002085772,"about_ca_topic_score_gemma":0.004696294,"domain_scores_codex":[0.9787366,0.0005648024,0.001820381,0.0002827387,0.01837353,0.000221988],"domain_scores_gemma":[0.9590496,0.0003407138,0.002944497,0.0004706789,0.03708411,0.0001104299],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003065954,0.0001497646,0.394637,0.000006298449,0.0004420844,0.000001260937,0.0002597854,0.1923769,0.001952435,0.04571362,0.000595176,0.3638349],"study_design_scores_gemma":[0.000510799,0.000166905,0.699993,0.00003973247,0.000108353,0.000002135492,0.000522715,0.279847,0.0008974867,0.01533662,0.002492195,0.00008298332],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8830246,0.00002092384,0.1043393,0.002605899,0.001369528,0.0003179335,0.000003384454,0.000002657219,0.008315815],"genre_scores_gemma":[0.9911476,0.000002604754,0.008453308,0.0001395161,0.000172468,0.000004845789,0.000003457622,0.000004394029,0.00007175762],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.363752,"threshold_uncertainty_score":0.9964349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.358731749205141,"score_gpt":0.5731939792993171,"score_spread":0.2144622300941761,"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."}}