{"id":"W2972651484","doi":"10.69554/vsux8674","title":"Moving beyond basic localisation: Culturally customising digital content","year":2015,"lang":"en","type":"article","venue":"Journal of cultural marketing strategy.","topic":"Management, Economics, and Public Policy","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Digital content; Content (measure theory); Business; Marketing; Knowledge management; Public relations; Sociology; Computer science; Multimedia; Political science","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":[],"consensus_categories":[],"category_scores_codex":[0.004022575,0.0003984553,0.0002581846,0.001028226,0.001901786,0.009887237,0.001021301,0.001636912,0.005171493],"category_scores_gemma":[0.009847661,0.0002430094,0.0003640776,0.0009965568,0.01115634,0.0120928,0.007684197,0.002222589,0.001171323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001570539,"about_ca_system_score_gemma":0.001898823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00218515,"about_ca_topic_score_gemma":0.002803951,"domain_scores_codex":[0.9967564,0.002154954,0.0001432036,0.0002090194,0.000473259,0.0002631794],"domain_scores_gemma":[0.9937004,0.003009412,0.0004186701,0.001631237,0.0007895221,0.0004508464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001327252,0.0001270306,0.006947997,0.0009093509,0.00003027769,0.001533485,0.1571143,0.001807321,0.01493408,0.5215805,0.005196909,0.289686],"study_design_scores_gemma":[0.00003889811,0.0002273251,0.008443192,0.001731469,0.00006182941,0.002547355,0.1236287,0.002632474,0.01008695,0.3877321,0.4627331,0.0001366274],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2844404,0.00361584,0.1179067,0.02096188,0.0005450019,0.0002703576,0.00008151351,0.0004181706,0.5717602],"genre_scores_gemma":[0.9527994,0.001953631,0.02777669,0.00241317,0.0001672277,0.00007514739,0.00004691289,0.0002044074,0.0145634],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009887237,"threshold_uncertainty_score":0.02127361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05690973477275781,"score_gpt":0.2491256231063626,"score_spread":0.1922158883336048,"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."}}