{"id":"W2038296180","doi":"10.1068/c11164r","title":"The Gift That Keeps on Giving: Land-Grant Universities and Regional Prosperity","year":2014,"lang":"en","type":"article","venue":"Environment and Planning C Government and Policy","topic":"Higher Education Governance and Development","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Land grant; Prosperity; Government (linguistics); Political science; Higher education; Exploratory analysis; The arts; Inclusion (mineral); Agriculture; Public administration; Land use; Economic growth; Sociology; Geography; Social science; Economics; Law; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004262401,0.00009433259,0.00007761178,0.00001276693,0.0009150766,0.0001036262,0.00006211335,0.00004137255,0.00002797382],"category_scores_gemma":[0.00001861129,0.00007040009,0.00001348944,0.0000247581,0.000250575,0.0000813959,0.00004263561,0.00007098157,0.00000435728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000148083,"about_ca_system_score_gemma":0.00005122486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006708332,"about_ca_topic_score_gemma":0.0000655262,"domain_scores_codex":[0.9990346,0.00007423446,0.00006753544,0.0001598778,0.0004738688,0.0001899294],"domain_scores_gemma":[0.9995849,0.0001785246,0.00006079821,0.00007295713,0.000002928378,0.0000998626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006709812,0.00004153645,0.8486805,0.00001251882,0.00003754654,0.000001539286,0.05075714,0.00001047958,0.00001542559,0.07585981,0.00949361,0.01502277],"study_design_scores_gemma":[0.0001719769,0.00002547039,0.4120414,0.00001403603,0.000005234444,5.623357e-7,0.004380364,0.00001607475,0.00001016706,0.0005251596,0.5827321,0.00007750199],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9648985,0.0006336612,0.00001228545,0.008949322,0.00008121873,0.00013293,0.000008124071,0.00001050796,0.02527349],"genre_scores_gemma":[0.9847465,0.002203072,0.0000508532,0.000654021,0.0002691895,0.000007829435,0.000002233439,0.000004287929,0.01206201],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5732385,"threshold_uncertainty_score":0.7038122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01715839797943667,"score_gpt":0.2586212353395187,"score_spread":0.241462837360082,"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."}}