{"id":"W1564102629","doi":"10.1111/j.1541-0064.2011.00384.x","title":"Building local nodes in a global sector: Clustering within the aeronautics industry in Montreal","year":2011,"lang":"en","type":"article","venue":"Canadian Geographies / Géographies canadiennes","topic":"Cultural Industries and Urban Development","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université TÉLUQ; Université du Québec à Montréal","funders":"Social Sciences and Humanities Research Council of Canada; Forskningsrådet om Hälsa, Arbetsliv och Välfärd; University of Toronto","keywords":"Metropolitan area; Business; Corporate governance; Point (geometry); Prime minister; Economic geography; Regional science; Political science; Economics; Geography; Finance; Politics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0004525166,0.0001709841,0.0001746792,0.001778747,0.004169846,0.002895614,0.0006938253,0.0003505393,0.005631016],"category_scores_gemma":[0.001438857,0.0001648061,0.0001515366,0.00369223,0.003175364,0.0008370932,0.002019991,0.0002762501,0.0003124058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0205121,"about_ca_system_score_gemma":0.01177194,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9247406,"about_ca_topic_score_gemma":0.9759156,"domain_scores_codex":[0.9994639,0.00009737846,0.000007635691,0.00007634021,0.00009334319,0.0002614813],"domain_scores_gemma":[0.9993654,0.00008648299,0.0001026792,0.00003421302,0.0001636504,0.0002475206],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001383136,0.0001115453,0.6190164,0.0002642646,0.00007566574,0.002411299,0.1437868,0.00724871,0.005425723,0.09540669,0.01363785,0.1124768],"study_design_scores_gemma":[0.00001701309,0.00005452139,0.7902794,0.0001076866,0.00003907542,0.0002253786,0.1301718,0.003762358,0.0005718757,0.003071676,0.07163863,0.00006042448],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9612775,0.0005998812,0.001209867,0.0009516805,0.00001035057,0.0000888675,0.0002732156,0.00004893951,0.0355398],"genre_scores_gemma":[0.9938956,0.0001738134,0.0003948968,0.00003295771,0.000002166042,0.000009206668,0.00008034116,0.000006777102,0.005404252],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9958302,"threshold_uncertainty_score":0.1514053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02639879681161608,"score_gpt":0.2306610442532803,"score_spread":0.2042622474416642,"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."}}