{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000551392,0.0001507781,0.0001269796,0.0003793472,0.0001972737,0.000131025,0.0007150251,0.0001426441,0.00001146282],"category_scores_gemma":[0.0003285041,0.0001479144,0.00007408004,0.00070425,0.00001061717,0.0006174596,0.00007402585,0.0003047181,0.00002644264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001795186,"about_ca_system_score_gemma":0.0003260207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002719368,"about_ca_topic_score_gemma":0.00008725833,"domain_scores_codex":[0.9987869,0.000004850015,0.0002441265,0.0002035855,0.000353716,0.0004068059],"domain_scores_gemma":[0.9990881,0.0000697262,0.0002318299,0.0001923098,0.0002131219,0.0002048803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[8.112182e-7,0.0001991482,0.09581684,0.0001170316,0.00008312705,7.890887e-8,0.002151323,0.000832221,0.00547338,0.8652027,0.02745159,0.00267174],"study_design_scores_gemma":[0.0002018299,0.00002442588,0.8870069,0.0003361347,0.00002853481,0.00001137453,0.0004075636,0.0044567,0.021044,0.001530407,0.08435849,0.0005936154],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9346739,0.000943103,0.00573025,0.03317567,0.01629659,0.0008968036,0.00001615384,0.0006564936,0.007611006],"genre_scores_gemma":[0.9906419,0.00001092218,0.007798687,0.0001746871,0.0003398578,0.00006615234,0.000003983289,0.00001936441,0.0009444839],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8636723,"threshold_uncertainty_score":0.6031775,"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."}}