{"meta":{"query_hash":"60c99e4afaf7","filters":{"venue":"International Journal of Advanced Research in Economics and Finance"},"cohort_total":5,"direct_labels_cover":0,"predictions_cover":5,"exported":5,"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/60c99e4afaf7","api":"https://metacan.xera.ac/api/v1/cohort?venue=International+Journal+of+Advanced+Research+in+Economics+and+Finance"},"results":[{"id":"W4297820146","doi":"10.55057/ijaref.2022.4.3.10","title":"The Impact of Russia-Ukraine Invasion on Oil and Gas Stocks in 7 Countries by Using Event Study Approach","year":2022,"lang":"en","type":"article","venue":"International Journal of Advanced Research in Economics and Finance","topic":"Global Energy Security and Policy","field":"Energy","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":"Event study; Sample (material); Abnormal return; Significant difference; Event (particle physics); Statistical analysis; Fossil fuel; Crude oil; Business; Structural break; Economics; Financial economics; Econometrics; Geography; Finance; Statistics; Engineering; Mathematics; Stock exchange","score_opus":0.034335219919523215,"score_gpt":0.3609959368186814,"score_spread":0.3266607168991582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297820146","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":[0.99701804,0.0012711993,0.0000018744531,0.00068054,0.00009454039,0.00004263346,0.000035024113,5.620913e-7,0.0008555701],"genre_scores_gemma":[0.97907305,0.020761706,0.000044243337,0.0000211641,0.000040889674,0.000007814549,0.0000022410563,0.0000060762577,0.000042837426],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989645,0.00014766154,0.00037867224,0.00011913861,0.00021970659,0.00017035713],"domain_scores_gemma":[0.9993667,0.00021658007,0.00021316209,0.000081424725,0.000092314986,0.000029794393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012431877,0.00007281412,0.00016378814,0.00022146814,0.00011554274,0.000039027196,0.000282718,0.00002162997,0.000003254761],"category_scores_gemma":[0.00007253707,0.000056533252,0.00003620072,0.000103377366,0.000105381325,0.00015206031,0.0001696615,0.00038117068,9.169717e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001229365,0.000380172,0.0016076877,0.0000031940344,0.000045402077,0.000020609103,0.0008035438,0.85866654,0.00009722601,0.06664477,0.00003058775,0.0704709],"study_design_scores_gemma":[0.027306594,0.011370108,0.06713208,0.00062876457,0.000015744263,0.0008090536,0.01351492,0.23805282,0.00080426905,0.14408101,0.49528363,0.0010010087],"about_ca_topic_score_codex":0.0019381774,"about_ca_topic_score_gemma":0.00044952804,"teacher_disagreement_score":0.6206137,"about_ca_system_score_codex":0.00041277823,"about_ca_system_score_gemma":0.00013532452,"threshold_uncertainty_score":0.29299572},"labels":[],"label_agreement":null},{"id":"W4318481876","doi":"10.55057/ijaref.2022.4.4.13","title":"The Impact of COVID-19 on the Malaysia Stock Market: Finance Sector","year":2023,"lang":"en","type":"article","venue":"International Journal of Advanced Research in Economics and Finance","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":1,"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 Bank of Canada","funders":"","keywords":"Index (typography); Stock market; Stock market index; Stock exchange; Business; Coronavirus disease 2019 (COVID-19); Pandemic; Economics; Finance; Geography","score_opus":0.1011976810746593,"score_gpt":0.3869584202913969,"score_spread":0.28576073921673756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318481876","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":[0.98008454,0.0014181102,0.00009373381,0.015623311,0.00055602804,0.00024398316,0.00019761705,0.0000044870626,0.0017781928],"genre_scores_gemma":[0.9459838,0.05290109,0.00007673271,0.00019670263,0.00015237313,0.000020047259,0.0000017986059,0.000019590581,0.0006478496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981421,0.00008079046,0.000937873,0.00027075585,0.0001362175,0.00043225443],"domain_scores_gemma":[0.9960742,0.0024886425,0.00083120645,0.00032754737,0.00019394553,0.00008442967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047494867,0.00014769498,0.00035063594,0.00064576947,0.00016836851,0.000112921676,0.0010304147,0.000075093136,0.000045552722],"category_scores_gemma":[0.002482384,0.00010637651,0.0001818622,0.000450033,0.0003098461,0.0003021134,0.00019221054,0.00058987434,0.000029553432],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026037877,0.00022786148,0.053704094,0.000038022707,0.00036241408,0.00012863529,0.0014476727,0.51755714,0.00012511335,0.36036474,0.016304458,0.047136087],"study_design_scores_gemma":[0.0031122894,0.00086567155,0.22689436,0.00017666513,0.0000023779137,0.00008180651,0.00038675178,0.10250439,0.00011970615,0.37228417,0.2932166,0.00035520893],"about_ca_topic_score_codex":0.00015800666,"about_ca_topic_score_gemma":0.000037590966,"teacher_disagreement_score":0.41505274,"about_ca_system_score_codex":0.0008498541,"about_ca_system_score_gemma":0.0004316472,"threshold_uncertainty_score":0.43379083},"labels":[],"label_agreement":null},{"id":"W4323924052","doi":"10.55057/ijaref.2023.5.1.9","title":"The Effects of Credit and Labor on Economic Growth in Indonesia","year":2023,"lang":"en","type":"article","venue":"International Journal of Advanced Research in Economics and Finance","topic":"Economic Growth and Fiscal Policies","field":"Economics, Econometrics and Finance","cited_by":0,"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":"Economics; Quarter (Canadian coin); Estimation; Credit history; Bank credit; Credit crunch; Monetary economics; Financial system","score_opus":0.02257784486979936,"score_gpt":0.29625838095020934,"score_spread":0.27368053608041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323924052","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":[0.99157375,0.002027798,0.0000025586316,0.004176519,0.0009772556,0.00012526278,0.000041292475,0.0000021600542,0.0010734056],"genre_scores_gemma":[0.90824825,0.091409564,0.000043776705,0.000059931648,0.000131766,0.000013283232,0.0000012491965,0.000011998742,0.00008016316],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9985991,0.000028544473,0.00082459016,0.00021437553,0.00004205092,0.00029129768],"domain_scores_gemma":[0.99837667,0.0009778477,0.00041682358,0.00011479053,0.000065002016,0.000048896753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014051192,0.00010256361,0.00033652526,0.000842569,0.00005630865,0.00006691135,0.0004060672,0.0000680827,0.0000011588384],"category_scores_gemma":[0.00032996654,0.00009771031,0.00005141036,0.00015150101,0.00024098126,0.00030983606,0.00012660814,0.0003424577,0.000013712141],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030858678,0.000055750406,0.2594644,0.000032992728,0.000043172855,0.000033388533,0.00038165704,0.0021994077,0.00002110925,0.7193897,0.00018574919,0.017884107],"study_design_scores_gemma":[0.0021531454,0.00033120235,0.71307534,0.00013920684,7.762198e-7,0.000014733342,0.0001722662,0.00311839,0.00032673075,0.2680399,0.012484934,0.00014334678],"about_ca_topic_score_codex":0.000084480416,"about_ca_topic_score_gemma":0.00007659214,"teacher_disagreement_score":0.45361093,"about_ca_system_score_codex":0.00018112532,"about_ca_system_score_gemma":0.000053228454,"threshold_uncertainty_score":0.3984511},"labels":[],"label_agreement":null},{"id":"W4323925569","doi":"10.55057/ijaref.2023.5.1.5","title":"The Differences of Bank Efficiency, Risk, And Performance Before and During the Covid-19 In Indonesia","year":2023,"lang":"en","type":"article","venue":"International Journal of Advanced Research in Economics and Finance","topic":"Financial Analysis and Corporate Governance","field":"Business, Management and Accounting","cited_by":0,"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":"Proxy (statistics); Coronavirus disease 2019 (COVID-19); Quarter (Canadian coin); Pandemic; Market liquidity; Statistics; Business; Mathematics; Geography; Medicine; Finance","score_opus":0.022917906892806643,"score_gpt":0.2735256099634169,"score_spread":0.25060770307061025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323925569","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":[0.9964217,0.0006773592,0.000008326943,0.002654969,0.00010982864,0.0000683153,0.000004270475,0.0000013292093,0.000053914362],"genre_scores_gemma":[0.9502527,0.04953592,0.000012958989,0.00004741769,0.00010392182,0.0000055569526,3.954914e-7,0.0000037878117,0.000037359914],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999182,0.000018103801,0.00034661268,0.00012412685,0.00017022663,0.00015895984],"domain_scores_gemma":[0.99917966,0.00019448096,0.00040874287,0.000068382986,0.00013925199,0.000009503119],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014415301,0.00006526905,0.00014551105,0.00025814955,0.00019524124,0.00012397426,0.0003107862,0.000022871684,6.736213e-7],"category_scores_gemma":[0.00020943058,0.000040719104,0.00002280353,0.00032213225,0.00026047466,0.00044080042,0.00019974665,0.0002432593,6.318456e-7],"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":[0.00022756337,0.000017654687,0.90158594,0.000028901839,0.000009812081,0.000015515978,0.0001400482,0.005302804,0.00002391856,0.012130401,0.000010778823,0.08050669],"study_design_scores_gemma":[0.0005278427,0.000027304024,0.9654401,0.000059848688,0.0000015707504,0.000007743308,0.00024786324,0.018543787,0.000009512962,0.010909263,0.004180243,0.00004489744],"about_ca_topic_score_codex":0.00023982958,"about_ca_topic_score_gemma":0.001283796,"teacher_disagreement_score":0.08046179,"about_ca_system_score_codex":0.00004696551,"about_ca_system_score_gemma":0.000053929885,"threshold_uncertainty_score":0.16604769},"labels":[],"label_agreement":null},{"id":"W4396921687","doi":"10.55057/ijaref.2024.6.1.20","title":"The Effect of TPF, NPF and CAR on Profitability with Financing as an Intervening Variable in Indonesian Islamic Banks during the Covid-19 Pandemic","year":2024,"lang":"en","type":"article","venue":"International Journal of Advanced Research in Economics and Finance","topic":"Islamic Finance and Communication","field":"Social Sciences","cited_by":1,"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":"Profitability index; Nonprobability sampling; Quarter (Canadian coin); Indonesian; Business; Islam; Population; Finance; Pandemic; Variables; Accounting; Coronavirus disease 2019 (COVID-19); Statistics; Medicine; Geography","score_opus":0.026572256040230457,"score_gpt":0.38376668302818195,"score_spread":0.3571944269879515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396921687","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":[0.99547315,0.0011237994,0.00003653665,0.0026188584,0.00013196463,0.00019842171,0.0000032070625,0.0000024988697,0.00041153832],"genre_scores_gemma":[0.9854278,0.014316796,0.00009372865,0.000021074111,0.000051706684,0.000021605289,4.7573465e-7,0.000005828662,0.00006096756],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9988013,0.000355583,0.0003259894,0.00015144616,0.00017942187,0.00018629621],"domain_scores_gemma":[0.99832106,0.001279858,0.00014639866,0.00013715835,0.000095544325,0.000019990843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004838898,0.00006715176,0.00014113351,0.0001623701,0.0002331842,0.00011799322,0.00046596234,0.00004575465,0.0000010702602],"category_scores_gemma":[0.00044658594,0.000043097938,0.000023884984,0.0001807043,0.00039817317,0.00042531753,0.000082828905,0.0005504174,2.6790985e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033138625,0.00008953989,0.24263433,0.00015368006,0.00006981607,0.000087428365,0.019495679,0.029016197,0.00062856043,0.32165846,0.000007554482,0.3828449],"study_design_scores_gemma":[0.008644014,0.004854526,0.49443078,0.00531139,0.0000211187,0.00043724154,0.015797608,0.021578966,0.0020154177,0.3969177,0.049247086,0.00074415066],"about_ca_topic_score_codex":0.00079628546,"about_ca_topic_score_gemma":0.004472581,"teacher_disagreement_score":0.38210073,"about_ca_system_score_codex":0.00043834047,"about_ca_system_score_gemma":0.0003589346,"threshold_uncertainty_score":0.24958053},"labels":[],"label_agreement":null}]}