{"meta":{"query_hash":"84c72376cb52","filters":{"venue":"Asian Journal of Accounting Research"},"cohort_total":2,"direct_labels_cover":0,"predictions_cover":2,"exported":2,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/84c72376cb52","api":"https://metacan.xera.ac/api/v1/cohort?venue=Asian+Journal+of+Accounting+Research"},"results":[{"id":"W2967845527","doi":"10.1108/ajar-09-2018-0032","title":"Detecting anomalies in financial statements using machine learning algorithm","year":2019,"lang":"en","type":"article","venue":"Asian Journal of Accounting Research","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Roads University","funders":"","keywords":"Anomaly detection; Finance; Econometrics; Computer science; Actuarial science; Data mining; Accounting; Business; Economics","score_opus":0.03589708034145628,"score_gpt":0.3285269592834637,"score_spread":0.2926298789420074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967845527","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9953438,0.00019627457,0.00064874394,0.00017173648,0.0006496898,0.00018847229,0.000002205774,0.00002102924,0.0027780419],"genre_scores_gemma":[0.99729484,0.000015625812,0.0010828897,0.000053847034,0.00142274,0.000002053041,0.000005373559,0.000033988017,0.000088655754],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99736804,0.00006976031,0.00071522297,0.00024060918,0.0009710598,0.0006353105],"domain_scores_gemma":[0.99846333,0.00006956555,0.00058367325,0.00013548344,0.0007284257,0.000019494266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041353847,0.00016802922,0.0003248512,0.0013165284,0.0003430033,0.00053375564,0.00038462985,0.00010254472,0.00018489512],"category_scores_gemma":[0.00094132766,0.00015800762,0.00010567101,0.0012789939,0.000058534235,0.002896235,0.00024516715,0.0011811027,0.00008004512],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016212442,0.00012113665,0.83760196,0.00026199108,0.000023146857,0.00016351555,0.00026556585,0.00073141477,0.003923482,0.00044082847,0.000112725036,0.15619212],"study_design_scores_gemma":[0.0057843425,0.00042296256,0.6623404,0.0029442364,0.00007215755,0.000117660544,0.007556168,0.27987394,0.0009327388,0.006142402,0.03280102,0.0010119701],"about_ca_topic_score_codex":0.0010302067,"about_ca_topic_score_gemma":0.00016307758,"teacher_disagreement_score":0.2791425,"about_ca_system_score_codex":0.00015850746,"about_ca_system_score_gemma":0.00013269918,"threshold_uncertainty_score":0.64433634},"labels":[],"label_agreement":null},{"id":"W4406647046","doi":"10.1108/ajar-12-2023-0413","title":"Voluntary cybersecurity risk disclosures and firms’ characteristics: the moderating role of the knowledge-intensive industry","year":2025,"lang":"en","type":"article","venue":"Asian Journal of Accounting Research","topic":"Risk Management in Financial Firms","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Business; Turnover; Accounting; Turnover intention; Computer security; Psychology; Management; Computer science; Social psychology; Economics; Organizational commitment","score_opus":0.017400920885755938,"score_gpt":0.29007559022981605,"score_spread":0.2726746693440601,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406647046","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98004013,0.00075748545,0.000037763282,0.005737941,0.000574968,0.00034449148,0.000005063093,0.000011973931,0.012490154],"genre_scores_gemma":[0.99801064,0.00007170898,0.00004948592,0.0003101636,0.0012807621,0.000006152364,0.0000012153924,0.000021993976,0.00024786207],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99767584,0.00016431179,0.0007406861,0.0002350337,0.0007330232,0.0004510922],"domain_scores_gemma":[0.9959429,0.0004061001,0.00093454914,0.00043068177,0.0022679127,0.000017878483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004773198,0.00019128401,0.0003546567,0.00051168364,0.0010449014,0.0006390416,0.0011011029,0.00015936646,0.000032394502],"category_scores_gemma":[0.005177777,0.000115711904,0.00015687774,0.0012168866,0.00047155117,0.00096598425,0.0012065147,0.0022867706,0.000010937461],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010142431,0.00008796246,0.92506146,0.00025508928,0.00016097167,0.000011907147,0.0015165517,0.000027381659,0.00025206298,0.010100445,0.009136362,0.05328839],"study_design_scores_gemma":[0.00045011027,0.000024465318,0.9283955,0.0009404235,0.00014920933,0.000008173782,0.017100088,0.0037018142,0.00010869289,0.03044684,0.018516539,0.00015814825],"about_ca_topic_score_codex":0.0006739979,"about_ca_topic_score_gemma":0.00018038861,"teacher_disagreement_score":0.05313024,"about_ca_system_score_codex":0.00006067095,"about_ca_system_score_gemma":0.00015462954,"threshold_uncertainty_score":0.9935007},"labels":[],"label_agreement":null}]}