{"id":"W2225747719","doi":"","title":"The Use and Benefits of Assistive Technologies as a Vocabulary Strategy for English as a Second Language Adult Students","year":2011,"lang":"en","type":"article","venue":"E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education","topic":"EFL/ESL Teaching and Learning","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Vocabulary; Assistive technology; Linguistics; English language; Psychology; Computer science; Mathematics education; Human–computer interaction","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001078626,0.0001885857,0.0001585311,0.0006902066,0.0004112468,0.001277372,0.0002895714,0.000472514,0.002630871],"category_scores_gemma":[0.008717658,0.0001014572,0.0002667321,0.000290123,0.0003408375,0.001562024,0.0007416257,0.0003327905,0.000477827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002943879,"about_ca_system_score_gemma":0.0005443259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001699839,"about_ca_topic_score_gemma":0.00327018,"domain_scores_codex":[0.9993842,0.0001989479,0.00006029261,0.00007497786,0.0001791466,0.0001024081],"domain_scores_gemma":[0.996695,0.002096368,0.000238498,0.0001096615,0.0004328231,0.0004276162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003822909,0.01072241,0.3206214,0.0005803749,0.0001391316,0.002152311,0.04138992,0.0005120608,0.02604322,0.002931938,0.002299917,0.5887844],"study_design_scores_gemma":[0.0005204111,0.01356223,0.8601087,0.0003466905,0.0006194981,0.002517994,0.07844665,0.003074691,0.02034917,0.002623903,0.01771453,0.0001156807],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957932,0.00009786882,0.00009376671,0.00007432094,0.000005975185,0.00001333746,0.00002031351,0.000006680757,0.003894598],"genre_scores_gemma":[0.9980801,0.0001464358,0.0004025278,0.00003092898,0.000003334461,0.00001611749,0.00002609295,0.000003582011,0.001290964],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002630871,"threshold_uncertainty_score":0.008801103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1254187490424807,"score_gpt":0.3045691289033655,"score_spread":0.1791503798608848,"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."}}