{"id":"W2902505046","doi":"10.22318/cscl2015.401","title":"The Development of Productive Vocabulary in Knowledge Building: A Longitudinal Study","year":2015,"lang":"en","type":"article","venue":"Computer Supported Collaborative Learning","topic":"Second Language Acquisition and Learning","field":"Psychology","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Vocabulary; Natural language processing; Linguistics","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":[0.001730462,0.0002143626,0.0003251939,0.0002572365,0.0002496277,0.00006649736,0.0002985637,0.00007271815,0.0005002185],"category_scores_gemma":[0.0001843362,0.0001725334,0.00003467349,0.001335795,0.0001018356,0.0001248092,0.0001661118,0.0005384934,0.00008832966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001141595,"about_ca_system_score_gemma":0.0004940396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001689287,"about_ca_topic_score_gemma":0.00009219436,"domain_scores_codex":[0.9971226,0.001185652,0.0005867148,0.0004930511,0.0002425028,0.000369467],"domain_scores_gemma":[0.9983454,0.0002836107,0.0002978839,0.0003065147,0.0006552872,0.0001113103],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0004842936,0.001171974,0.2603264,0.00001412028,0.0004925631,0.0002885519,0.6513829,0.00107341,0.0001322053,0.001510607,0.00218913,0.08093379],"study_design_scores_gemma":[0.004121384,0.001458855,0.6977165,0.0000707177,0.00003607851,0.00003341548,0.2522596,0.00131919,0.0003835702,0.00006815971,0.0420426,0.0004899173],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9803951,0.002261792,0.0115263,0.00008436801,0.0007461742,0.0006893103,8.216376e-7,0.00008124253,0.004214858],"genre_scores_gemma":[0.9938201,4.762523e-7,0.004862732,0.00005064448,0.0001982461,0.0001044478,0.00001281787,0.00002593978,0.0009246378],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4373901,"threshold_uncertainty_score":0.7035708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05159756204863051,"score_gpt":0.365044131213659,"score_spread":0.3134465691650285,"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."}}