{"id":"W2008599905","doi":"10.1007/s10961-014-9364-9","title":"Geographic proximity and university–industry interaction: the case of Mexico","year":2014,"lang":"en","type":"article","venue":"The Journal of Technology Transfer","topic":"Innovation and Knowledge Management","field":"Business, Management and Accounting","cited_by":88,"is_retracted":false,"has_abstract":false,"ca_institutions":"Saint Mary's University","funders":"International Development Research Centre","keywords":"Absorptive capacity; Tacit knowledge; Economic geography; Knowledge transfer; Regional science; Business; Local government; Industrial organization; Technology transfer; Geographical distance; Government (linguistics); Channel (broadcasting); Knowledge management; Marketing; Geography; Sociology; Political science; Engineering; Telecommunications; Computer science; Public administration; International trade","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004951145,0.00009381932,0.0002687441,0.00146784,0.003486909,0.003123904,0.0006908785,0.001120148,0.0119582],"category_scores_gemma":[0.002393964,0.0001282192,0.0002359944,0.003020583,0.001356183,0.001455122,0.002212562,0.0008036064,0.0002876653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002648123,"about_ca_system_score_gemma":0.001540212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2387342,"about_ca_topic_score_gemma":0.3261003,"domain_scores_codex":[0.9996644,0.0001284775,0.000008624974,0.00003318186,0.00002264348,0.0001426782],"domain_scores_gemma":[0.9978995,0.001098584,0.0005204843,0.00005091155,0.0001381064,0.000292418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000590809,0.000994782,0.8752873,0.000134091,0.0002022408,0.007329511,0.02734705,0.004301524,0.0008613247,0.04686872,0.004095942,0.03198664],"study_design_scores_gemma":[0.0001545657,0.0002029985,0.7902853,0.0001698505,0.0003612524,0.001061394,0.1767325,0.005088606,0.0004070179,0.00591545,0.01955842,0.00006256137],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925731,0.0002054524,0.00005901982,0.000721398,0.00000252026,0.000004108963,0.00004808295,0.000002846872,0.006383402],"genre_scores_gemma":[0.9986046,0.0002026969,0.00004803014,0.00002771625,0.000004423728,0.000003582397,0.00003623804,0.000001404455,0.001071421],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2387342,"threshold_uncertainty_score":0.4746893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01148964411301757,"score_gpt":0.2109020645325156,"score_spread":0.199412420419498,"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."}}