{"id":"W4400888461","doi":"10.3102/ip.24.2168485","title":"Spatial Context Matters: Socioeconomic Advantage, Competition, and Academic Performance","year":2024,"lang":"en","type":"article","venue":"","topic":"Higher Education Governance and Development","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Socioeconomic status; Competition (biology); Spatial contextual awareness; Context (archaeology); Computer science; Regional science; Data science; Geography; Artificial intelligence; Sociology; Demography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009430103,0.0002221449,0.0004356453,0.0009511763,0.0009963413,0.003019696,0.0005367185,0.0008728456,0.01091724],"category_scores_gemma":[0.004205216,0.00012952,0.0004123759,0.001728837,0.002223779,0.001188381,0.002011058,0.0004432462,0.0006955728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009128341,"about_ca_system_score_gemma":0.001552916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01883547,"about_ca_topic_score_gemma":0.04158444,"domain_scores_codex":[0.9988263,0.0004364291,0.00007002585,0.0001467727,0.0001706114,0.0003498155],"domain_scores_gemma":[0.9956272,0.001380184,0.001043536,0.0002036345,0.000256118,0.001489388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003350918,0.0005207185,0.9789959,0.00003198951,0.0001597042,0.0002971782,0.0008948012,0.0008853659,0.0005361605,0.005641127,0.0006482437,0.01105362],"study_design_scores_gemma":[0.00002597228,0.0001818853,0.9880053,0.00002318259,0.00006114046,0.00010731,0.004926222,0.001051807,0.0001685864,0.00432553,0.0011089,0.00001410333],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929134,0.0001659034,0.0001027634,0.0004933543,0.000008835495,0.000002550835,0.00007030831,0.000003520539,0.006239329],"genre_scores_gemma":[0.9996742,0.00002969767,0.00001471093,0.00001217496,0.000005908629,6.838457e-7,0.00001860206,8.868799e-7,0.0002429818],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01883547,"threshold_uncertainty_score":0.03745168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0138601146554127,"score_gpt":0.3125981405915307,"score_spread":0.298738025936118,"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."}}