{"id":"W4385492976","doi":"10.1177/00420980231186234","title":"Sector connectors, specialists and scrappers: How cities use civic capital to compete in high-technology markets","year":2023,"lang":"en","type":"article","venue":"Urban Studies","topic":"Cultural Industries and Urban Development","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Marketing buzz; Leverage (statistics); Business; Variety (cybernetics); Entrepreneurship; Marketing; Complementary assets; Industrial organization; Economic geography; Economics; Finance; Advertising","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.00119404,0.0002499121,0.0002657702,0.001552337,0.008254612,0.00974794,0.0009373351,0.0008942884,0.00456873],"category_scores_gemma":[0.001969255,0.000232491,0.0002451967,0.00250581,0.008815954,0.002659806,0.005207774,0.0007752706,0.0002822692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02085043,"about_ca_system_score_gemma":0.01263259,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.6073236,"about_ca_topic_score_gemma":0.8601033,"domain_scores_codex":[0.9980863,0.0006730422,0.00002229105,0.0001470639,0.0002091475,0.0008621375],"domain_scores_gemma":[0.9979862,0.00036945,0.0002284199,0.0001161101,0.0001899153,0.001109983],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0002253486,0.0002175805,0.2975294,0.000151067,0.0000814119,0.001785704,0.4959801,0.002059712,0.002259901,0.127926,0.01252136,0.05926253],"study_design_scores_gemma":[0.00005800717,0.0001143107,0.2345114,0.0001630276,0.0000563201,0.0002540316,0.6348408,0.002496331,0.0005245889,0.009065934,0.1178246,0.0000906312],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9542881,0.0002696197,0.0006097825,0.001686099,0.00001299428,0.00002801877,0.00005450384,0.00001722325,0.04303382],"genre_scores_gemma":[0.9972152,0.00009008365,0.0001819106,0.00007508732,0.000001540132,0.000005124924,0.00001816205,0.000006536961,0.002406264],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6073236,"threshold_uncertainty_score":0.7899779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07884705574014889,"score_gpt":0.2969822907329384,"score_spread":0.2181352349927895,"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."}}