{"id":"W4378838751","doi":"10.1007/s11192-023-04729-y","title":"Assessing the quest of SMEs in pivoting for new technological ventures: comparing the patenting indexes of seven developed cities","year":2023,"lang":"en","type":"article","venue":"Scientometrics","topic":"Innovation and Knowledge Management","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Institute for Advanced Research","funders":"Ministry of Science and Technology, Taiwan","keywords":"Commit; Business; Upgrade; Silicon valley; Industrial organization; Cluster (spacecraft); Competitive advantage; Marketing; Sorting; Entrepreneurship; Computer science; Database; Finance","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":[0.001492441,0.0002369864,0.0004255976,0.004508452,0.0006176821,0.003168996,0.0004441462,0.0005043477,0.00118438],"category_scores_gemma":[0.006094251,0.0001460152,0.000440572,0.006464191,0.0009530967,0.001292646,0.00172546,0.0003699226,0.0002603805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001195326,"about_ca_system_score_gemma":0.001360819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02713091,"about_ca_topic_score_gemma":0.06012953,"domain_scores_codex":[0.9993176,0.0001349825,0.00006795024,0.00009254664,0.0002019911,0.0001848336],"domain_scores_gemma":[0.9940854,0.002141244,0.001378427,0.0003440783,0.00139157,0.0006592181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003783555,0.00008919246,0.9806523,0.00005720513,0.0002007054,0.0001479126,0.001647495,0.001626707,0.001464287,0.002515818,0.0002021243,0.01101785],"study_design_scores_gemma":[0.00001673384,0.00008131381,0.9916747,0.0000130332,0.0000791892,0.00002781648,0.004147064,0.001879781,0.000796455,0.0004977575,0.0007754813,0.0000106657],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987447,0.00004053633,0.0000875406,0.00001998221,6.759035e-7,0.000003584786,0.00008693361,0.000001952251,0.001014057],"genre_scores_gemma":[0.9995042,0.00003224247,0.00007493288,0.000003261922,9.546716e-7,0.000002088641,0.0002058627,0.000001261092,0.0001751698],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9954916,"threshold_uncertainty_score":0.05394596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1530829460319046,"score_gpt":0.3483397474152686,"score_spread":0.195256801383364,"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."}}