{"id":"W2479133563","doi":"10.1075/lllt.38.15sch","title":"Chapter 11. Language selection, control,  and conceptual-lexical development  in bilinguals and multilinguals","year":2013,"lang":"en","type":"book-chapter","venue":"Language learning and language teaching","topic":"Second Language Learning and Teaching","field":"Arts and Humanities","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Selection (genetic algorithm); Linguistics; Cognition; Control (management); Computer science; Psychology; Context (archaeology); Artificial intelligence; History","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":[0.0002046017,0.0002745599,0.0001964788,0.0003917948,0.0007728976,0.00179375,0.0002389341,0.0003594725,0.01056798],"category_scores_gemma":[0.0002853737,0.0001099781,0.0001397773,0.0004121193,0.00139259,0.0011685,0.0008392464,0.0007996534,0.001154572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001390022,"about_ca_system_score_gemma":0.001511165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005604193,"about_ca_topic_score_gemma":0.01352058,"domain_scores_codex":[0.9998833,0.00002402976,0.000005759601,0.00002352452,0.00004047613,0.00002285221],"domain_scores_gemma":[0.999904,0.00005229854,0.000006253739,0.000003754368,0.00001704989,0.00001671319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001110346,0.0001617556,0.003686094,0.001067139,0.000009843773,0.001104143,0.05511259,0.0002774434,0.005964783,0.5410928,0.03539051,0.3560218],"study_design_scores_gemma":[0.00001113423,0.00008713407,0.01497095,0.0009714128,0.000008594183,0.00163892,0.01132321,0.0002318696,0.0020276,0.08575626,0.882954,0.00001896654],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.04620179,0.1222818,0.002990686,0.003306941,0.000685522,0.00004571435,0.0001354231,0.00007681382,0.8242753],"genre_scores_gemma":[0.3714651,0.09641495,0.004397489,0.001216883,0.0005140624,0.0001017227,0.0003105149,0.0001297131,0.5254495],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01056798,"threshold_uncertainty_score":0.03535336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01370062360377615,"score_gpt":0.246086849975085,"score_spread":0.2323862263713088,"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."}}