{"id":"W4410484490","doi":"10.1016/j.joi.2025.101686","title":"Measuring the university–industry–government relations synthesized by the Triple Helix and the diversity","year":2025,"lang":"en","type":"article","venue":"Journal of Informetrics","topic":"University-Industry-Government Innovation Models","field":"Business, Management and Accounting","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institute on Governance","funders":"Nanjing University; National Natural Science Foundation of China","keywords":"Triple helix; Diversity (politics); Government (linguistics); Computer science; Political science; Business; Data science; Library science; Engineering management; Engineering; Linguistics; Stereochemistry; Chemistry; Philosophy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002160559,0.0001185576,0.0001709309,0.0002975896,0.001498788,0.0002528752,0.0007077271,0.000163937,0.00007017067],"category_scores_gemma":[0.001313551,0.00006530282,0.0001098061,0.002381463,0.0001867851,0.001341898,0.0007481354,0.0009911945,0.000007058722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003750457,"about_ca_system_score_gemma":0.00006683643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001120256,"about_ca_topic_score_gemma":0.00000987252,"domain_scores_codex":[0.9985001,0.00004097347,0.0003107598,0.00008131255,0.0009082792,0.0001585649],"domain_scores_gemma":[0.9980074,0.0006916131,0.0007685046,0.0001999394,0.0003185447,0.00001397374],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001410229,0.00021668,0.2333347,0.00009211099,0.001152578,0.00002362864,0.001027053,0.003045851,0.00006206791,0.3191838,0.4157097,0.02474154],"study_design_scores_gemma":[0.006457349,0.00002034709,0.03761597,0.0001216368,0.000831678,0.00001257775,0.01833331,0.007725863,0.00008900959,0.00193879,0.9265987,0.0002547925],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7024268,0.0004056101,0.02410274,0.07531756,0.001198065,0.0009092484,0.00002673357,0.00004667477,0.1955666],"genre_scores_gemma":[0.9888159,0.0000642171,0.00005694795,0.002369252,0.0001307163,2.816928e-7,7.482438e-7,0.000004719152,0.008557222],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.510889,"threshold_uncertainty_score":0.9998011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02322332636499285,"score_gpt":0.1913355452925803,"score_spread":0.1681122189275874,"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."}}