{"id":"W3024413611","doi":"","title":"THE IMPACT OF MACROECONOMIC FACTORS ON MSCI PRICE INDEX IN INDUSTRIAL COUNTRIES","year":2018,"lang":"en","type":"dissertation","venue":"Osuva (University of Vaasa)","topic":"Global Trade and Competitiveness","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Index (typography); Economics; Econometrics; Macroeconomics; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001611013,0.0001934524,0.0003281281,0.0003000986,0.0001804854,0.00005800219,0.0005061516,0.0002229542,0.0004407728],"category_scores_gemma":[0.00002855514,0.0001757344,0.0001868926,0.0003069883,0.0001232866,0.0003947733,0.00006324571,0.0002001221,0.00006381273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001187222,"about_ca_system_score_gemma":0.0001369199,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008953164,"about_ca_topic_score_gemma":0.004408006,"domain_scores_codex":[0.9992137,0.00001439034,0.0001651205,0.0002103934,0.0001900227,0.0002063511],"domain_scores_gemma":[0.9990357,0.00006929024,0.000536542,0.000196686,0.0001503971,0.00001138149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00467566,0.0003923529,0.9495944,0.0004612135,0.0006244748,0.00002954713,0.001516292,0.000239042,0.0001482466,0.0290117,0.01006211,0.003244962],"study_design_scores_gemma":[0.001058692,0.00007160575,0.9652923,0.0002915952,0.00005896749,1.606409e-7,0.007734976,0.00008897835,0.00005697451,0.0007915528,0.02430348,0.0002507104],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.962912,0.00002416447,0.000002202816,0.00006211893,0.0004408714,0.0001966383,0.00003414045,0.00001427879,0.03631359],"genre_scores_gemma":[0.9984754,0.00003710079,0.00000185126,0.00001199122,0.0001480425,2.340882e-7,0.0001603278,0.00001203043,0.001152968],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03556346,"threshold_uncertainty_score":0.9976463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02231857923037453,"score_gpt":0.2265805778920527,"score_spread":0.2042619986616782,"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."}}