{"id":"W4241972920","doi":"10.31235/osf.io/jrk67","title":"Academic Inbreeding at the Canadian Engineering Schools","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Higher Education Governance and Development","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Inbreeding; Index (typography); Diversity (politics); Productivity; Sample (material); Quality (philosophy); Psychology; Sociology; Demography; Political science; Economics; Computer science; Law; Chemistry; Physics; Population; Economic growth","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007956875,0.0001155979,0.00009863561,0.00007048339,0.0008779087,0.0001748537,0.0005410118,0.0003378321,0.002337553],"category_scores_gemma":[0.0002408495,0.00009000208,0.00004204919,0.0001541667,0.00007895927,0.00007164817,0.0002367353,0.0006794196,0.0007708268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002369509,"about_ca_system_score_gemma":0.004380677,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6064268,"about_ca_topic_score_gemma":0.8150363,"domain_scores_codex":[0.9987791,0.00003060259,0.0001674797,0.0002288067,0.0004127322,0.0003812675],"domain_scores_gemma":[0.9992949,0.00004486263,0.0000798029,0.0001955358,0.0001080154,0.0002768562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001226373,0.000003568249,0.05150183,0.00001673161,0.00004595749,0.000001627146,0.03092473,0.0001533903,0.00001470171,0.02353831,0.8922488,0.001549142],"study_design_scores_gemma":[0.00002043027,0.00000106361,0.02604231,0.00006276301,0.000004877621,2.587474e-7,0.0006236056,0.00001244779,0.00004847232,0.0007765096,0.9722455,0.0001617688],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3905413,0.000904026,0.0001430525,0.1070867,0.01228571,0.001015877,0.00003558706,0.0002632991,0.4877245],"genre_scores_gemma":[0.9239448,0.0003388764,0.0009041604,0.002056646,0.002453463,0.00009397502,0.00001227486,0.00001670398,0.07017916],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5334035,"threshold_uncertainty_score":0.9985744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03522587567278895,"score_gpt":0.3249697859805155,"score_spread":0.2897439103077266,"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."}}