{"id":"W2949007741","doi":"10.5206/cjsotl-rcacea.2019.1.7994","title":"Analyzing Implicit Science and Math Outcomes in Engineering and Technology Programs","year":2019,"lang":"en","type":"article","venue":"The Canadian Journal for the Scholarship of Teaching and Learning","topic":"Engineering Education and Curriculum Development","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Seneca Polytechnic; Queen's University; University of British Columbia","funders":"","keywords":"Novelty; Context (archaeology); Computer science; Transferability; Mathematics education; Bridging (networking); Mathematics; Psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.006351841,0.000351827,0.0004491018,0.00263001,0.001394861,0.003081963,0.001042715,0.0006015277,0.00419915],"category_scores_gemma":[0.04432354,0.000196381,0.00032864,0.00226266,0.002101215,0.001732579,0.004637757,0.001346959,0.0004485785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004764898,"about_ca_system_score_gemma":0.004529709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05333317,"about_ca_topic_score_gemma":0.1203469,"domain_scores_codex":[0.9956324,0.0008018503,0.0002824865,0.0004004485,0.002264393,0.0006185479],"domain_scores_gemma":[0.9643427,0.01421525,0.00912629,0.001547698,0.006922598,0.003845427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002673618,0.000853992,0.9350702,0.0001043891,0.00004481806,0.00007499381,0.01709137,0.000736015,0.001475736,0.001589204,0.0002966071,0.04239532],"study_design_scores_gemma":[0.000009004246,0.0001911951,0.991759,0.0000212052,0.00001038555,0.00001219303,0.004785833,0.0006928702,0.001098483,0.0006253105,0.000780937,0.00001370216],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970516,0.00001505864,0.0003649094,0.00005293341,0.000002114842,0.00002709544,0.0001032523,0.000006568833,0.002376387],"genre_scores_gemma":[0.997572,0.00002095196,0.0003734412,0.00001012045,0.000002585124,0.00004569003,0.0002702493,0.000005403787,0.001699506],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05333317,"threshold_uncertainty_score":0.1060455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009425863482609993,"score_gpt":0.2426291034111655,"score_spread":0.2332032399285555,"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."}}