{"id":"W4377242234","doi":"10.1177/02676583231156307","title":"Feature reassembly and L1 preemption: Acquiring CLLD in L2 Italian and L2 Romanian","year":2023,"lang":"en","type":"article","venue":"Second language Research","topic":"Phonetics and Phonology Research","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Clitic; Romanian; Feature (linguistics); Linguistics; Second-language acquisition; Syntax; Computer science; Language transfer; First language; Psychology; Natural language processing; Natural language; Comprehension approach","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008932468,0.0004358667,0.00035879,0.0004663161,0.0004714453,0.000988961,0.0004016125,0.0005142694,0.002342448],"category_scores_gemma":[0.002814552,0.0003084489,0.0003186593,0.0003027701,0.001127959,0.0005732824,0.001127201,0.000874967,0.0007653014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006603619,"about_ca_system_score_gemma":0.00058136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007649836,"about_ca_topic_score_gemma":0.00924768,"domain_scores_codex":[0.9994967,0.0001310626,0.00003136022,0.000128433,0.0001078237,0.0001045748],"domain_scores_gemma":[0.998978,0.000279349,0.0003538049,0.0001661696,0.0001287113,0.0000938895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00157023,0.001008982,0.477221,0.0003401104,0.00008510044,0.004833948,0.1102429,0.0005199898,0.3396848,0.002697303,0.0005845396,0.06121104],"study_design_scores_gemma":[0.00005945556,0.001198352,0.9347165,0.00004921223,0.00005842492,0.005391378,0.01520879,0.001142945,0.03265727,0.0006189928,0.008811429,0.00008731496],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980221,0.00003311124,0.0001772967,0.0000281479,7.476492e-7,0.000006123679,0.00002099018,0.000007815427,0.001703647],"genre_scores_gemma":[0.9980606,0.0000491089,0.0004042623,0.00002874003,0.000001570863,0.00000993171,0.00008929295,0.00001849012,0.001338057],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007649836,"threshold_uncertainty_score":0.01521063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07231710467685623,"score_gpt":0.4476433063479808,"score_spread":0.3753262016711246,"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."}}