{"id":"W2070736469","doi":"10.1016/j.ibusrev.2009.03.001","title":"Rating versus ranking: What is the best way to reduce response and language bias in cross-national research?","year":2009,"lang":"en","type":"article","venue":"International Business Review","topic":"Cultural Differences and Values","field":"Psychology","cited_by":159,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"Australian Research Council","keywords":"Ranking (information retrieval); Likert scale; Rank (graph theory); Psychology; Multinational corporation; Point (geometry); Rating scale; Selection bias; Econometrics; Social psychology; Computer science; Applied psychology; Statistics; Artificial intelligence; Mathematics; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.436523,0.001068064,0.00598441,0.00435064,0.002973146,0.006525268,0.003189321,0.003159573,0.004715101],"category_scores_gemma":[0.6508672,0.0009678164,0.003222732,0.007738899,0.004490213,0.009872506,0.00251028,0.004087532,0.001761671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002713261,"about_ca_system_score_gemma":0.005521483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002344582,"about_ca_topic_score_gemma":0.005775762,"domain_scores_codex":[0.4765141,0.4481048,0.03688065,0.008803873,0.02753078,0.002165834],"domain_scores_gemma":[0.279016,0.5555176,0.05587429,0.03495603,0.07149374,0.0031423],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003587872,0.000613792,0.05164718,0.02101695,0.005749644,0.0001858323,0.01102877,0.0004727086,0.004200544,0.02000366,0.03350427,0.8479889],"study_design_scores_gemma":[0.00923423,0.01001372,0.3441894,0.05688161,0.03422273,0.003348109,0.0293172,0.02330029,0.03091644,0.2311035,0.2254201,0.002052704],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2795746,0.09179641,0.3509615,0.1935748,0.01884867,0.009822478,0.001538098,0.001835726,0.05204789],"genre_scores_gemma":[0.6321686,0.01694229,0.3015258,0.03105506,0.00598355,0.007234568,0.0005226369,0.0005801654,0.003987238],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.563477,"threshold_uncertainty_score":0.6948675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3569368196218795,"score_gpt":0.5415442500898541,"score_spread":0.1846074304679746,"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."}}