{"id":"W2476861039","doi":"10.4018/978-1-61520-883-8.ch027","title":"Addressing Cross-Linguistic Influence and Related Cultural Factors Using Computer-Assisted Language Learning (CALL)","year":2010,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"EFL/ESL Teaching and Learning","field":"Arts and Humanities","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université TÉLUQ; Université du Québec à Montréal; Université de Sherbrooke","funders":"","keywords":"Linguistics; Computer science; Ontology; Relation (database); Language acquisition; Foreign language; Work (physics); Natural language processing; Engineering","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":[0.001743471,0.0003278108,0.0002187746,0.0006933762,0.001213987,0.005150768,0.0006868587,0.0005780796,0.00407424],"category_scores_gemma":[0.00311724,0.0001011593,0.0002190733,0.000858597,0.00200668,0.002938171,0.003397176,0.0009496016,0.0004240551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001536716,"about_ca_system_score_gemma":0.00176263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003277099,"about_ca_topic_score_gemma":0.006310838,"domain_scores_codex":[0.9987102,0.0007209884,0.00004336409,0.0001074673,0.0003156441,0.0001024742],"domain_scores_gemma":[0.9972265,0.002094309,0.0001602115,0.0001574778,0.0002282574,0.0001331737],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006800966,0.0004679277,0.02064898,0.0007562814,0.0000478919,0.001453291,0.07471388,0.001886872,0.008023304,0.08618336,0.004000721,0.8017495],"study_design_scores_gemma":[0.00004048047,0.0006797191,0.0707259,0.002113203,0.0002559648,0.004938188,0.2909809,0.02366187,0.02724113,0.1266721,0.4524978,0.000192739],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6296929,0.004752581,0.06411071,0.004738314,0.0001655114,0.0001358872,0.00003779401,0.0002617069,0.2961046],"genre_scores_gemma":[0.965615,0.002027731,0.01744637,0.0004422562,0.00002455317,0.00004697269,0.00003841888,0.0000370143,0.01432181],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005150768,"threshold_uncertainty_score":0.01362973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05849197349084392,"score_gpt":0.3111187724774766,"score_spread":0.2526267989866327,"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."}}