{"id":"W1523584058","doi":"10.3386/w16869","title":"Math or Science? Using Longitudinal Expectations Data to Examine the Process of Choosing a College Major","year":2011,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Higher Education Research Studies","field":"Social Sciences","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Social Sciences and Humanities Research Council of Canada; Spencer Foundation; Andrew W. Mellon Foundation; National Science Foundation","keywords":"Mathematics education; Longitudinal data; Process (computing); Econometrics; Psychology; Mathematics; Computer science; Statistics; Data mining; Programming language","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":[0.006548202,0.0001489009,0.0002537503,0.0009289095,0.0006055858,0.001699608,0.0004994965,0.0005692393,0.004004009],"category_scores_gemma":[0.02626281,0.0002075421,0.0003787504,0.001049408,0.0003837206,0.001420703,0.00108952,0.001610827,0.001159133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006676132,"about_ca_system_score_gemma":0.0008019502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01731354,"about_ca_topic_score_gemma":0.02010986,"domain_scores_codex":[0.9981896,0.0009003586,0.0001174935,0.0002427851,0.0002847832,0.0002650619],"domain_scores_gemma":[0.9689105,0.01278287,0.01125551,0.001706779,0.002399646,0.002944643],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000125634,0.0001441078,0.9905463,0.000007478179,0.00002253219,0.00002828176,0.000511515,0.0003833206,0.00008115896,0.00126993,0.001127085,0.005752626],"study_design_scores_gemma":[0.00003295359,0.0002617966,0.9790653,0.00003235299,0.00002649193,0.00006140977,0.003064308,0.009668571,0.0005076458,0.004304107,0.002940692,0.00003427121],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912148,0.00007900745,0.001759074,0.000949094,0.00001996627,0.00004190643,0.001747527,0.00001598646,0.004172596],"genre_scores_gemma":[0.9954584,0.00006819139,0.0005512554,0.0001087601,0.00001876521,0.00005026075,0.002246979,0.00000643053,0.0014911],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01731354,"threshold_uncertainty_score":0.0346306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7341943367588416,"score_gpt":0.6509517075993074,"score_spread":0.08324262915953418,"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."}}