{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":2,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":2,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"96d135974b9c","filters":{"venue":"International Journal of Asian Language Processing"}},"results":[{"id":"W4382699275","doi":"10.1142/s2717554523500066","title":"Tapping the Potential of Coherence and Syntactic Features in Neural Models for Automatic Essay Scoring","year":2022,"lang":"en","type":"article","venue":"International Journal of Asian Language Processing","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Coherence (philosophical gambling strategy); Artificial intelligence; Feature engineering; Embedding; Feature (linguistics); Natural language processing; Artificial neural network; Machine learning; Deep learning; Linguistics; Mathematics","authors":[{"name":"Xinying Qiu","is_ca":false},{"name":"Shuxuan Liao","is_ca":false},{"name":"Jiajun Xie","is_ca":false},{"name":"Jian‐Yun Nie","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01238061958306724,"gpt":0.2786262898719821,"spread":0.2662456702889148,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005048726,0.00006391867,0.0001177752,0.0001758363,0.00008884496,0.0001452097,0.0007864216,0.00001368975,0.000003684587],"category_scores_gemma":[0.00007833695,0.00005019493,0.00004405319,0.0001088257,0.00002107667,0.0006395285,0.0002059781,0.0002142005,2.501229e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007659675,"about_ca_system_score_gemma":0.0001377641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001852146,"about_ca_topic_score_gemma":0.000004483468,"domain_scores_codex":[0.9989912,0.000053405,0.0003201269,0.0001111834,0.0004187561,0.0001053134],"domain_scores_gemma":[0.9993156,0.0000705027,0.0003850162,0.00008138454,0.0001243408,0.00002315335],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003211339,0.00004512557,0.0002983582,0.00007713043,0.00003853473,0.0001350658,0.02060223,0.08938669,0.004240504,0.001391609,0.00000554966,0.8837471],"study_design_scores_gemma":[0.0003723423,0.00003420895,0.00103671,0.0001670421,0.000007754395,0.0006898398,0.00367211,0.990694,0.0003136164,0.002951907,0.000002948083,0.00005745099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3364314,0.002379317,0.6581371,0.002367809,0.0004358015,0.0001013754,0.000002021651,0.00001190855,0.0001331931],"genre_scores_gemma":[0.9716778,0.00000193633,0.02810814,0.00009890603,0.00008939761,0.000006004505,3.12309e-7,0.000004982051,0.00001248173],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9013074,"threshold_uncertainty_score":0.204689,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4406883348","doi":"10.1142/s2717554524500176","title":"The Power of Personalized Datasets: Advancing Chinese Composition Writing for Elementary School through Targeted Model Fine-Tuning","year":2025,"lang":"en","type":"article","venue":"International Journal of Asian Language Processing","topic":"Educational Technology and Assessment","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Composition (language); Power (physics); Mathematics education; Computer science; Psychology; Physics; Literature; Art","authors":[{"name":"Wenyi Xie","is_ca":false},{"name":"J. Li","is_ca":false},{"name":"Xinran Zheng","is_ca":false},{"name":"K. B. Song","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.007021506712353659,"gpt":0.3488968130148321,"spread":0.3418753063024784,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005214539,0.00009718375,0.000137239,0.0001576315,0.0002043033,0.0001337484,0.0009533321,0.00003595629,0.000007199657],"category_scores_gemma":[0.0002174028,0.00007232232,0.00008094208,0.0001891264,0.00005792538,0.0009318,0.000126889,0.0002037675,2.552032e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001207536,"about_ca_system_score_gemma":0.0003864524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003163849,"about_ca_topic_score_gemma":0.000003295441,"domain_scores_codex":[0.9988838,0.00003178269,0.0004842594,0.0001320163,0.0003263372,0.0001418201],"domain_scores_gemma":[0.998701,0.0001588792,0.0005109192,0.0001266283,0.0004749106,0.00002771355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008587337,0.001193243,0.01616094,0.0003939758,0.001708626,0.000139736,0.02412187,0.008638265,0.219713,0.1370912,0.009171224,0.5808092],"study_design_scores_gemma":[0.009763278,0.0004550471,0.008402437,0.006847167,0.0002193327,0.0006607915,0.05047804,0.6890465,0.05761943,0.1720396,0.003406994,0.001061419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06308648,0.002818387,0.9232937,0.009979052,0.00035315,0.00009848734,0.00003449625,0.00002133397,0.0003149896],"genre_scores_gemma":[0.7428207,0.00001374944,0.2566509,0.0003856668,0.00006943546,0.000006327944,0.00002647961,0.000004195717,0.00002260163],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6804082,"threshold_uncertainty_score":0.2949219,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}