{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.08373339,0.0007238756,0.0007804724,0.01130219,0.007245704,0.00861216,0.003038037,0.001385867,0.002062364],"category_scores_gemma":[0.1793934,0.0003882861,0.0006045889,0.0183271,0.003000099,0.005255168,0.005000827,0.0008806546,0.0002950637],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.06061976,"about_ca_system_score_gemma":0.07261917,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7889238,"about_ca_topic_score_gemma":0.8292955,"domain_scores_codex":[0.9265699,0.02700633,0.002420462,0.001857093,0.03871256,0.003433678],"domain_scores_gemma":[0.7571916,0.09754968,0.01812483,0.006871251,0.1074771,0.01278565],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001267301,0.0005416488,0.4679503,0.002490601,0.0006851249,0.0005144834,0.02720564,0.02659561,0.001390489,0.04465059,0.02573276,0.4009754],"study_design_scores_gemma":[0.0002035316,0.001207907,0.74347,0.001547187,0.0004325789,0.0001810379,0.05895579,0.09836337,0.002688432,0.01562531,0.07695265,0.0003722276],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8510336,0.003332647,0.01477154,0.01767181,0.0002177862,0.002361583,0.003313838,0.0003871825,0.10691],"genre_scores_gemma":[0.9860873,0.001033192,0.00924858,0.000249439,0.00003562744,0.0004010339,0.0007894164,0.00004549975,0.002109936],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9886978,"threshold_uncertainty_score":0.4428298,"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."}}