{"meta":{"query_hash":"571d12b8ce65","filters":{"venue":"Journal of Business Thought (online)"},"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/571d12b8ce65","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Business+Thought+%28online%29"},"results":[{"id":"W2786255432","doi":"10.18311/jbt/2019/23355","title":"The Role of Precision Timing in Stock Market Price Discovery when Trading through Distributed Ledgers","year":2019,"lang":"en","type":"article","venue":"Journal of Business Thought (online)","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":9,"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; Stock market; Distributed ledger; Ledger; Business; Financial economics; Economics; Monetary economics; Econometrics; Computer science; Accounting; Computer security; Blockchain; Geography","score_opus":0.030032949407366564,"score_gpt":0.23353764636248622,"score_spread":0.20350469695511966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786255432","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":[0.8872899,0.00043839097,0.10551246,0.0005502987,0.000104315426,0.00010888981,0.00010602345,0.0006803855,0.0052094655],"genre_scores_gemma":[0.9904524,0.00006369751,0.008926555,0.000029930337,0.00002518405,0.000019361354,0.00003233184,0.00003817481,0.00041245864],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99270225,0.0022239967,0.00054073747,0.0012152202,0.0025350368,0.00078275613],"domain_scores_gemma":[0.8830729,0.08149542,0.016473915,0.012611875,0.004236338,0.0021096647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009525562,0.00046897496,0.0006935935,0.0012208162,0.0010228612,0.004351473,0.0013788772,0.001025699,0.0017934514],"category_scores_gemma":[0.09332424,0.00063365785,0.00036736083,0.0011736674,0.0023729606,0.006658525,0.0023721727,0.0021155002,0.00035908443],"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":[0.007141528,0.0014098261,0.2509748,0.000568055,0.00038824938,0.002983143,0.0047820685,0.22389051,0.09345485,0.11289402,0.0016880209,0.29982486],"study_design_scores_gemma":[0.00057941204,0.003647049,0.12232592,0.00015869518,0.00047234187,0.0024146615,0.0029062422,0.6233731,0.12311115,0.11084165,0.00971985,0.0004499868],"about_ca_topic_score_codex":0.0025349257,"about_ca_topic_score_gemma":0.0019955435,"teacher_disagreement_score":0.009525562,"about_ca_system_score_codex":0.0012876912,"about_ca_system_score_gemma":0.0019039758,"threshold_uncertainty_score":0.050376594},"labels":[],"label_agreement":null},{"id":"W4405644166","doi":"10.18311/jbt/2024/44468","title":"A Study on Micro-Segmentation of Retail Customers Using K-Means Clustering","year":2024,"lang":"en","type":"article","venue":"Journal of Business Thought (online)","topic":"Customer churn and segmentation","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":"Innovation Cluster (Canada)","funders":"","keywords":"Cluster analysis; Segmentation; Business; Market segmentation; Marketing; Computer science; Artificial intelligence","score_opus":0.06449499949697056,"score_gpt":0.3070108336129689,"score_spread":0.2425158341159983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405644166","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":[0.974175,0.00015304815,0.02243803,0.0002004126,0.000010737297,0.00012045364,0.0001896723,0.00005222474,0.0026603912],"genre_scores_gemma":[0.98609865,0.00006120804,0.012990173,0.00003263103,0.0000075479957,0.000043449967,0.00021183024,0.00001386857,0.0005406309],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982974,0.0006799254,0.000116151576,0.00036389582,0.00036383438,0.00017879759],"domain_scores_gemma":[0.9951084,0.002883838,0.0005881481,0.0004602668,0.0007776827,0.00018156425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001531948,0.00026531756,0.0004977251,0.0016732982,0.0013734826,0.0017330098,0.0006675021,0.0006155766,0.001574568],"category_scores_gemma":[0.007114871,0.00023566998,0.00051392807,0.0032598763,0.0007131256,0.0016044939,0.000616353,0.000597877,0.00040626895],"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":[0.0007300724,0.00069140655,0.7097029,0.00031082053,0.0003059172,0.0004600732,0.03882713,0.017558321,0.0052657058,0.013878907,0.002538277,0.2097305],"study_design_scores_gemma":[0.000035470664,0.0007784741,0.6522232,0.00019971794,0.0001453791,0.0012218313,0.05069835,0.26648468,0.0042879703,0.015062689,0.008671245,0.00019097824],"about_ca_topic_score_codex":0.011804975,"about_ca_topic_score_gemma":0.012106965,"teacher_disagreement_score":0.011804975,"about_ca_system_score_codex":0.0011241415,"about_ca_system_score_gemma":0.0008111758,"threshold_uncertainty_score":0.023472488},"labels":[],"label_agreement":null}]}