{"id":"W3171892779","doi":"10.1101/2021.06.01.446616","title":"Building Back More Equitable STEM Education: Teach Science by Engaging Students in Doing Science","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Columbia College","funders":"","keywords":"Curriculum; Equity (law); Apprenticeship; Engineering ethics; Psychology; Public relations; Mathematics education; Sociology; Pedagogy; Medical education; Political science; Engineering; Medicine","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":[0.02826044,0.0006073496,0.001005411,0.002944929,0.00129634,0.004719174,0.001806814,0.001648839,0.01166943],"category_scores_gemma":[0.06541448,0.0003880326,0.002708732,0.002532567,0.002895151,0.004940697,0.007706746,0.004120261,0.001258802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002700263,"about_ca_system_score_gemma":0.005777624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002659539,"about_ca_topic_score_gemma":0.005972942,"domain_scores_codex":[0.9730098,0.02040345,0.001283416,0.001294354,0.003151284,0.0008577486],"domain_scores_gemma":[0.944873,0.04187776,0.004464383,0.003779421,0.002433169,0.002572231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001042518,0.002246982,0.01702242,0.07989784,0.006861457,0.0001283012,0.0203543,0.00182738,0.00189217,0.05124727,0.02323326,0.7942461],"study_design_scores_gemma":[0.002849868,0.004771193,0.05817446,0.2242765,0.01561254,0.0003971366,0.02563752,0.00269877,0.007684242,0.1264296,0.5311881,0.0002801183],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.414971,0.2515766,0.1018631,0.1122081,0.009399416,0.006911138,0.004706698,0.001589416,0.09677454],"genre_scores_gemma":[0.8518381,0.04630673,0.07101619,0.02038484,0.0007433197,0.004062448,0.0009199805,0.0002390385,0.00448938],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02826044,"threshold_uncertainty_score":0.1494573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01507951274962416,"score_gpt":0.2980461749015754,"score_spread":0.2829666621519513,"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."}}