{"meta":{"query_hash":"4028bb28922e","filters":{"venue":"Finance Research Open"},"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/4028bb28922e","api":"https://metacan.xera.ac/api/v1/cohort?venue=Finance+Research+Open"},"results":[{"id":"W4408567212","doi":"10.1016/j.finr.2025.100006","title":"Supervised learning models, statistical models or hybrid models? A prediction of clean energy stock based on fear and fundamental factors","year":2025,"lang":"en","type":"article","venue":"Finance Research Open","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"","keywords":"Computer science; Statistical learning; Artificial intelligence; Machine learning; Stock (firearms); Predictive modelling; Statistical model; Engineering","score_opus":0.4432901866504693,"score_gpt":0.48784877063457066,"score_spread":0.04455858398410134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408567212","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22512662,0.0021590078,0.7585986,0.004045041,0.0002090965,0.00006690849,0.00056108995,0.0007251563,0.008508399],"genre_scores_gemma":[0.9418628,0.0009047242,0.052291587,0.00028513893,0.00018067929,0.00007399268,0.00031074753,0.000032594104,0.0040577967],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980396,0.00008786291,0.000009116605,0.000045958765,0.00003221783,0.000020990146],"domain_scores_gemma":[0.9992267,0.00046624488,0.00011092965,0.00007851498,0.00008593219,0.000031760726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010981655,0.0004521641,0.00049087865,0.00033850217,0.00010840861,0.00078478333,0.00070543186,0.0005896928,0.0011498985],"category_scores_gemma":[0.0023934839,0.00021826058,0.0004527399,0.0003801126,0.00032899645,0.0012473203,0.0002689545,0.00079374766,0.0002777035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000085545274,0.00011826997,0.008640933,0.000096307995,0.00016910963,0.000068093,0.00009419705,0.8776315,0.00078254216,0.03480959,0.003139517,0.074364424],"study_design_scores_gemma":[0.0000041338094,0.000015984695,0.0006895487,0.0000086707105,0.000007351651,0.0000100998295,0.000009666882,0.98642844,0.00010241806,0.012335977,0.00038251642,0.0000052602354],"about_ca_topic_score_codex":0.0043341555,"about_ca_topic_score_gemma":0.005031282,"teacher_disagreement_score":0.0043341555,"about_ca_system_score_codex":0.00034367872,"about_ca_system_score_gemma":0.0004453294,"threshold_uncertainty_score":0.008617878},"labels":[],"label_agreement":null},{"id":"W4413479467","doi":"10.1016/j.finr.2025.100047","title":"Volatility discovery in G-7 stock markets based on evidence from realized kernels","year":2025,"lang":"en","type":"article","venue":"Finance Research Open","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","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":"Price discovery; Volatility (finance); Stock (firearms); Financial economics; Econometrics; Business; Monetary economics; Economics; Engineering","score_opus":0.14302462684848852,"score_gpt":0.3908380483696591,"score_spread":0.24781342152117056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413479467","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99416214,0.00016498307,0.0045612217,0.000089011286,0.0000061186392,0.000007732885,0.00008097709,0.000024161613,0.00090371934],"genre_scores_gemma":[0.9993881,0.000055915294,0.00039723213,0.00000569889,0.000006179094,0.0000014674534,0.000079669946,0.000002721672,0.000063048516],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994111,0.00012152655,0.000055987282,0.00013662725,0.00016609253,0.00010873465],"domain_scores_gemma":[0.99187887,0.004105199,0.0023216528,0.0006503687,0.00071954104,0.00032443515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018108529,0.00027137782,0.00044962243,0.0020183588,0.00032885847,0.0016169832,0.0006355164,0.0006232487,0.0014672141],"category_scores_gemma":[0.0130319055,0.00017673655,0.0005179447,0.0011872143,0.00085431506,0.0019777676,0.00091848185,0.0006532955,0.00014848325],"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":[0.0012019855,0.00020282326,0.84602726,0.0001996464,0.00058705313,0.0023632273,0.00087439886,0.027502075,0.014100029,0.04920949,0.001258072,0.056473937],"study_design_scores_gemma":[0.000063800704,0.00028692238,0.59145176,0.000059272606,0.00026409523,0.0009525876,0.00087639724,0.36457556,0.0060769864,0.0339994,0.0012931039,0.00010009701],"about_ca_topic_score_codex":0.003325856,"about_ca_topic_score_gemma":0.0020680458,"teacher_disagreement_score":0.003325856,"about_ca_system_score_codex":0.00042735823,"about_ca_system_score_gemma":0.0003734489,"threshold_uncertainty_score":0.0095767975},"labels":[],"label_agreement":null},{"id":"W4415956441","doi":"10.1016/j.finr.2025.100073","title":"Does the yield curve affect the systemic risk between the stocks of FinTech and traditional finance companies?","year":2025,"lang":"en","type":"article","venue":"Finance Research Open","topic":"FinTech, Crowdfunding, Digital Finance","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Social connectedness; Systemic risk; Affect (linguistics); Yield curve; Yield (engineering); Financial market","score_opus":0.09944394463848207,"score_gpt":0.34157477917332046,"score_spread":0.2421308345348384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415956441","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9951669,0.0001949728,0.00016578213,0.00029053635,0.000009000503,0.000006660312,0.0004020417,0.000011557061,0.0037526516],"genre_scores_gemma":[0.99891806,0.00006131155,0.00004943936,0.000037833157,0.000008419616,0.0000019679287,0.00034586844,0.0000021784792,0.0005748861],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995289,0.000055107903,0.000041738218,0.00010313436,0.00016896662,0.00010222155],"domain_scores_gemma":[0.9955473,0.000807133,0.0023318534,0.00023642405,0.0005137245,0.00056357065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090532535,0.00028525593,0.00024220237,0.0010342735,0.00041447018,0.0015260479,0.00027962946,0.00055775006,0.004446982],"category_scores_gemma":[0.0055661937,0.000098289485,0.00040553958,0.001154406,0.00048237178,0.0013328227,0.00087573135,0.00055083394,0.0007091062],"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":[0.000114452436,0.000026742326,0.9889282,0.000009032997,0.000051416242,0.00010562341,0.0001057628,0.0004843962,0.00026648297,0.00039908226,0.0003958095,0.009113126],"study_design_scores_gemma":[0.000003560511,0.00007513763,0.99778444,0.000008265404,0.00001348973,0.00005329093,0.00017463426,0.0008109929,0.00019078665,0.00033120363,0.0005477774,0.000006606568],"about_ca_topic_score_codex":0.0137816835,"about_ca_topic_score_gemma":0.014270961,"teacher_disagreement_score":0.0137816835,"about_ca_system_score_codex":0.0011105806,"about_ca_system_score_gemma":0.0005230189,"threshold_uncertainty_score":0.027402937},"labels":[],"label_agreement":null},{"id":"W4416333281","doi":"10.1016/j.finr.2025.100072","title":"Evasive shareholder meetings, meeting announcement lag, and stock price crash risk","year":2025,"lang":"en","type":"article","venue":"Finance Research Open","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Shareholder; Stock price; Stock (firearms); Shareholder value; Crash","score_opus":0.1046869288896011,"score_gpt":0.34735284375350534,"score_spread":0.24266591486390424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416333281","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9982838,0.00009918865,0.00041778723,0.00013911306,0.000008909899,0.000009843669,0.00022554304,0.000006839214,0.0008088978],"genre_scores_gemma":[0.9994054,0.00004067211,0.00009639853,0.0000141827195,0.000018589219,0.0000065559,0.00020077541,9.463341e-7,0.00021663707],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990707,0.00020029841,0.00012206222,0.00015705568,0.00025223475,0.00019760686],"domain_scores_gemma":[0.9510528,0.013647087,0.030090587,0.0015684162,0.000981766,0.0026593467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022545282,0.00029903676,0.00026328652,0.0008781119,0.00027919255,0.0009900611,0.00042791545,0.00067610276,0.0038261919],"category_scores_gemma":[0.022705745,0.00014269422,0.00034207976,0.0006008573,0.00030015784,0.00084547343,0.0008817964,0.001212776,0.0005571722],"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":[0.00012897774,0.00007428016,0.99433315,0.000011133213,0.000067898465,0.000055976696,0.000108284155,0.0005264369,0.0002779537,0.00020109038,0.00019824787,0.0040164646],"study_design_scores_gemma":[0.0000074991276,0.00019713349,0.9957891,0.000010106415,0.00003968385,0.00007179221,0.0003434407,0.002527192,0.00031645744,0.00040155178,0.00028628617,0.000009822124],"about_ca_topic_score_codex":0.0019699878,"about_ca_topic_score_gemma":0.0038460956,"teacher_disagreement_score":0.0038261919,"about_ca_system_score_codex":0.0003073865,"about_ca_system_score_gemma":0.00027776495,"threshold_uncertainty_score":0.012799919},"labels":[],"label_agreement":null},{"id":"W7114894119","doi":"10.1016/j.finr.2025.100082","title":"Greenwashing and the efficiency of new information price discovery","year":2025,"lang":"en","type":"article","venue":"Finance Research Open","topic":"Auditing, Earnings Management, Governance","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity Western University; Western University; Simon Fraser University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Greenwashing; Corporate governance; Stock (firearms); Stock price; Price discovery","score_opus":0.024341575704331233,"score_gpt":0.3055512530631353,"score_spread":0.2812096773588041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7114894119","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9948914,0.0001673547,0.0009962699,0.00035582838,0.0000060374828,0.00001440773,0.00005632098,0.000016629156,0.0034956855],"genre_scores_gemma":[0.99942577,0.000035575715,0.0001961582,0.000019110532,0.0000050291933,0.0000018012595,0.000027544844,0.0000021415756,0.00028685035],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975546,0.00068788993,0.0003246187,0.0003709759,0.000742874,0.00031906978],"domain_scores_gemma":[0.8795942,0.04898225,0.055493083,0.008625911,0.0053394437,0.001965128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0067734555,0.00012724672,0.00027355054,0.0014937815,0.00042269984,0.002826786,0.00037361623,0.00059127004,0.0020717315],"category_scores_gemma":[0.051426888,0.00016803702,0.00028914076,0.0010424481,0.0016604884,0.0027590732,0.001421252,0.0011761452,0.00025157697],"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":[0.00038461827,0.00033626438,0.93766963,0.00007245499,0.00017053437,0.00014962496,0.0017848549,0.004705573,0.0028960612,0.0051848106,0.00052010565,0.04612555],"study_design_scores_gemma":[0.000017482784,0.00027650336,0.9768298,0.00003158555,0.00004453686,0.000079194535,0.0018868413,0.008626984,0.004180475,0.005655875,0.002329729,0.000040977586],"about_ca_topic_score_codex":0.004281787,"about_ca_topic_score_gemma":0.0057763946,"teacher_disagreement_score":0.0067734555,"about_ca_system_score_codex":0.0013072516,"about_ca_system_score_gemma":0.00063205167,"threshold_uncertainty_score":0.035821915},"labels":[],"label_agreement":null}]}