{"id":"W2103828285","doi":"10.24908/pceea.v0i0.4684","title":"IDENTIFYING DISCIPLINE-SPECIFIC VOCABULARY ON ENGINEERING EXAMS","year":2012,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Educational Technology and Assessment","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Vocabulary; tf–idf; Computer science; Test (biology); Measure (data warehouse); Engineering education; Natural language processing; Term (time); Mathematics education; Artificial intelligence; Data science; Psychology; Engineering; Linguistics; Data mining; Engineering management","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.004330225,0.0004035969,0.0006766107,0.008392604,0.0005472187,0.00201628,0.0005863329,0.000751102,0.001995725],"category_scores_gemma":[0.04977772,0.0001194199,0.0004380652,0.00451961,0.0004548138,0.001897877,0.002022852,0.0005596344,0.000955725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004552159,"about_ca_system_score_gemma":0.0006670458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002835883,"about_ca_topic_score_gemma":0.005281031,"domain_scores_codex":[0.9946741,0.001432981,0.0009393067,0.0007371395,0.001943928,0.0002724574],"domain_scores_gemma":[0.9629436,0.01600607,0.006955574,0.002532344,0.01014851,0.001413941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004134974,0.000389712,0.5393798,0.000673937,0.0001316074,0.0006506413,0.006880256,0.001898096,0.04581546,0.001858516,0.002409172,0.3994992],"study_design_scores_gemma":[0.00002331936,0.0008396983,0.9569805,0.0001705104,0.00008574717,0.0008405159,0.005196697,0.006759686,0.01599508,0.002596736,0.01042769,0.00008377378],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9739273,0.0004850072,0.01664598,0.00006213636,0.00003551064,0.000230967,0.001050907,0.0001805055,0.007381751],"genre_scores_gemma":[0.9744236,0.0003297626,0.01971488,0.00004430995,0.00003277378,0.0002264354,0.002599247,0.00005611466,0.002572855],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008392604,"threshold_uncertainty_score":0.02290076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01187401359281956,"score_gpt":0.2290356583752344,"score_spread":0.2171616447824149,"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."}}