{"id":"W2996747268","doi":"10.1111/grow.12357","title":"Identifying configurations of multiple co‐located clusters by analyzing within‐ and between‐cluster linkages","year":2019,"lang":"en","type":"article","venue":"Growth and Change","topic":"Innovation and Knowledge Management","field":"Business, Management and Accounting","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"American Association of Geographers","keywords":"Beijing; Cluster (spacecraft); Linkage (software); Economic geography; Business cluster; Business; Government (linguistics); Industrial organization; Marketing; Empirical research; Regional science; Computer science; Geography; China","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":[],"consensus_categories":[],"category_scores_codex":[0.002353167,0.0002269538,0.0003527939,0.004364761,0.001287462,0.003016547,0.0007465507,0.0006313797,0.003097202],"category_scores_gemma":[0.01181941,0.0002362167,0.0004075914,0.006061198,0.001715993,0.002165198,0.002081499,0.0002952576,0.0002053616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001913029,"about_ca_system_score_gemma":0.001165337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01002818,"about_ca_topic_score_gemma":0.01138314,"domain_scores_codex":[0.9979191,0.0008901898,0.0001230125,0.0005034158,0.0002779178,0.0002863472],"domain_scores_gemma":[0.9926355,0.003820685,0.001498504,0.0007701119,0.0008471485,0.0004279877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002735922,0.00007430057,0.9225919,0.00009298357,0.0001638626,0.0005983885,0.004481797,0.01421991,0.001779665,0.03623082,0.0004045614,0.01908834],"study_design_scores_gemma":[0.00005656604,0.0002138303,0.7214921,0.00009364862,0.0002285323,0.0004944869,0.04139394,0.1814073,0.002787728,0.04805159,0.003690686,0.00008962993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910274,0.00003246961,0.006536206,0.00004746438,0.00000194289,0.00004544858,0.0001447509,0.00001425121,0.002150196],"genre_scores_gemma":[0.9979275,0.000009171632,0.001877438,0.000001790283,6.47493e-7,0.00001499935,0.00008250644,0.000001451105,0.00008456055],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01002818,"threshold_uncertainty_score":0.0199396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03664034824816164,"score_gpt":0.2492061973754104,"score_spread":0.2125658491272488,"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."}}