{"id":"W2117108960","doi":"10.2190/w5ar-dypw-40kx-fl99","title":"Essay Assessment with Latent Semantic Analysis","year":2003,"lang":"en","type":"article","venue":"Journal of Educational Computing Research","topic":"Topic Modeling","field":"Computer Science","cited_by":92,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Latent semantic analysis; Automatic summarization; Readability; Computer science; Natural language processing; Semantics (computer science); Semantic similarity; Quality (philosophy); Similarity (geometry); Probabilistic latent semantic analysis; Information retrieval; Artificial intelligence; Semantic analysis (machine learning); Data science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004750196,0.00008457356,0.0002108307,0.0008286462,0.0002514436,0.0002604539,0.0007573529,0.0000295309,0.00008795105],"category_scores_gemma":[0.0003319425,0.00006332683,0.0001044388,0.001810435,0.00004727871,0.0002362053,0.000100777,0.0005497957,0.000008536576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002941337,"about_ca_system_score_gemma":0.002267713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001821817,"about_ca_topic_score_gemma":0.000004451732,"domain_scores_codex":[0.9971724,0.0004738472,0.0004172108,0.0002263532,0.00138923,0.000320918],"domain_scores_gemma":[0.996682,0.001158647,0.0002324946,0.0003502218,0.001414124,0.000162498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001079242,0.000693151,0.428379,0.00004260975,0.00087513,0.00003969997,0.00171049,0.155987,0.0002691659,0.4057334,0.001050098,0.005209435],"study_design_scores_gemma":[0.0008823348,0.0004986371,0.4436463,0.0002191557,0.0001350275,0.0004851992,0.0003905435,0.519187,0.0002502717,0.03275435,0.001198023,0.0003532046],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3988748,0.0001532396,0.5921488,0.00555769,0.0002451319,0.00006602339,1.302664e-7,0.000005529467,0.002948547],"genre_scores_gemma":[0.8270764,0.000004114575,0.1724519,0.00004376891,0.0001451814,8.753771e-7,4.260003e-7,0.000004471648,0.0002728282],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4282016,"threshold_uncertainty_score":0.4022826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08015701964176432,"score_gpt":0.4292517564698892,"score_spread":0.3490947368281249,"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."}}