{"id":"W2962098473","doi":"10.1007/s11192-019-03181-1","title":"Territorial innovation models: to be or not to be, that’s the question","year":2019,"lang":"en","type":"article","venue":"Scientometrics","topic":"Regional Development and Policy","field":"Social Sciences","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal","funders":"H2020 Societal Challenges; Eusko Jaurlaritza","keywords":"Variety (cybernetics); Field (mathematics); Regional science; Divergence (linguistics); Urban agglomeration; Computer science; Economic geography; Data science; Sociology; Knowledge management; Geography; Linguistics; Artificial intelligence; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.02885869,0.0005886722,0.001912119,0.002259454,0.002962634,0.015011,0.002706315,0.005326892,0.01172769],"category_scores_gemma":[0.09012993,0.0003862791,0.0008766401,0.004647298,0.01970921,0.02820986,0.003683761,0.005389969,0.003340202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006338802,"about_ca_system_score_gemma":0.008070217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01165722,"about_ca_topic_score_gemma":0.01342723,"domain_scores_codex":[0.9855508,0.009079512,0.0003538107,0.002103654,0.001811656,0.001100612],"domain_scores_gemma":[0.9351584,0.03615801,0.004682706,0.01038304,0.008595172,0.005022655],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007078284,0.00006559417,0.006908794,0.0003188278,0.0002016443,0.00003103968,0.001535238,0.001179905,0.0001126886,0.8860871,0.06180123,0.04168707],"study_design_scores_gemma":[0.00003730699,0.00001986119,0.002793644,0.0003170335,0.00004166987,0.00006746874,0.004037473,0.002133118,0.0001468319,0.9248853,0.06548221,0.00003803098],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"review","genre_scores_codex":[0.04537468,0.01067573,0.02482742,0.8271466,0.003046104,0.00004777378,0.0009538851,0.0002556538,0.08767202],"genre_scores_gemma":[0.9362254,0.005941931,0.01017308,0.03371759,0.003112567,0.0001171471,0.0005412764,0.0002940604,0.009876856],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.9977406,"threshold_uncertainty_score":0.1526212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2503429839231957,"score_gpt":0.4287641124956658,"score_spread":0.1784211285724701,"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."}}