{"id":"W4380449803","doi":"10.5267/j.ijdns.2023.4.012","title":"Big data analytics techniques and their impacts on reducing information asymmetry: Evidence from Jordan","year":2023,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Organizational and Employee Performance","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Big data; Stock exchange; Computer science; Descriptive statistics; Data science; Panel data; Data analysis; Analytics; Variables; Business; Data mining; Econometrics; Finance; Statistics; Economics; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01182449,0.0003671187,0.0003400673,0.002654859,0.001032699,0.003900993,0.0007364555,0.0007218589,0.002791303],"category_scores_gemma":[0.02986082,0.0001650839,0.000714559,0.0030224,0.001593179,0.003888133,0.002193219,0.001197848,0.0004776298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00132589,"about_ca_system_score_gemma":0.004939846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002343576,"about_ca_topic_score_gemma":0.00272589,"domain_scores_codex":[0.9922503,0.003771942,0.0004723551,0.0002465976,0.002779488,0.000479386],"domain_scores_gemma":[0.9546139,0.02596311,0.006919437,0.001588568,0.008954973,0.00196017],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001144062,0.002187647,0.2388615,0.006070567,0.0006397074,0.001165071,0.01705521,0.002623896,0.001463189,0.02481063,0.011438,0.6925405],"study_design_scores_gemma":[0.0006027158,0.003548555,0.5860508,0.01704427,0.001216832,0.002427965,0.1352133,0.01242392,0.01044152,0.03943268,0.1912965,0.000300947],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9061649,0.02655385,0.003843924,0.01255753,0.0001191557,0.000276222,0.0003172483,0.00006814476,0.05009905],"genre_scores_gemma":[0.9798402,0.0134737,0.003390083,0.001393139,0.000103088,0.00006589109,0.0002088488,0.00001833779,0.001506725],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01182449,"threshold_uncertainty_score":0.06253463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0899279777547719,"score_gpt":0.3234715529777455,"score_spread":0.2335435752229736,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}