{"id":"W2617446031","doi":"10.1177/0142723717708493","title":"The development of a bilingual vocabulary measure for Armenian–English children","year":2017,"lang":"en","type":"article","venue":"First Language","topic":"Reading and Literacy Development","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Armenian; Vocabulary; Identification (biology); Measure (data warehouse); Psychology; Linguistics; Reliability (semiconductor); Validity; Natural language processing; Computer science; Developmental psychology; Psychometrics","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":[0.0006902,0.0001426698,0.0001779841,0.0000409187,0.0008616752,0.0000929176,0.0005840799,0.0000833107,0.00009242401],"category_scores_gemma":[0.000395236,0.00009779799,0.0000813256,0.00003320984,0.00008318285,0.00004965913,0.00008250268,0.0001093309,0.00004376973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003130584,"about_ca_system_score_gemma":0.0001133482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006133297,"about_ca_topic_score_gemma":0.0002815287,"domain_scores_codex":[0.9988929,0.00003021781,0.0003310778,0.0002541082,0.0001752319,0.0003164726],"domain_scores_gemma":[0.9986876,0.0001584466,0.0002329283,0.0007628017,0.00009800866,0.00006026804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0004369396,0.0003196174,0.09290145,0.00008855035,0.000873154,0.00002444181,0.57609,0.000002131917,0.0003409005,0.002164414,0.01841825,0.3083402],"study_design_scores_gemma":[0.002397817,0.0001045774,0.7078711,0.0001889038,0.00004979951,0.00001614795,0.006714189,0.000008094205,0.004630853,0.00007308906,0.2774794,0.0004661004],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9824467,0.0007809216,0.0002388658,0.0001778654,0.001069748,0.0004853413,0.00003512526,0.00005555615,0.01470989],"genre_scores_gemma":[0.9904202,0.000002164339,0.002888811,0.00003482872,0.0003314088,0.0001103924,0.00003865503,0.00002556506,0.00614797],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6149696,"threshold_uncertainty_score":0.6627396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01746315969646701,"score_gpt":0.3094137517559071,"score_spread":0.2919505920594401,"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."}}