{"id":"W2332721209","doi":"10.5539/ijel.v6n2p105","title":"Studying Languages in the Linguistic Landscape of Lijiang Old Town","year":2016,"lang":"en","type":"article","venue":"International Journal of English Linguistics","topic":"Multilingual Education and Policy","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Linguistic landscape; Scripting language; Linguistics; Qualitative research; Field (mathematics); Linguistic analysis; Qualitative analysis; Quantitative research; Sociology; Geography; Psychology; Computer science; Social science; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001373944,0.00006600096,0.0001313593,0.0002051592,0.0000488398,0.00005240609,0.0006298595,0.00004891921,0.0001927225],"category_scores_gemma":[0.212412,0.00004050205,0.00007341652,0.0001355481,0.00009953063,0.00003721763,0.00002504918,0.0001482752,0.000002765821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006575236,"about_ca_system_score_gemma":0.0002875518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003374193,"about_ca_topic_score_gemma":0.0002784917,"domain_scores_codex":[0.9986172,0.0001401206,0.0004480447,0.000067022,0.0005934938,0.0001340781],"domain_scores_gemma":[0.9901286,0.001231735,0.000374509,0.00009392103,0.008112158,0.00005903462],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001640179,0.0007684607,0.142577,0.00002381684,0.0002001937,0.0001358569,0.4929511,0.00008400608,0.00007271633,0.3185051,0.01654408,0.02797366],"study_design_scores_gemma":[0.0007727536,0.00005437738,0.00748649,0.0002203056,0.00002361844,0.000001202733,0.0270965,0.000007591286,0.0001336851,0.001745899,0.9623548,0.0001027259],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3688979,0.0005808597,0.00009548376,0.00127307,0.0258377,0.0001708304,0.00003740921,0.00002282436,0.603084],"genre_scores_gemma":[0.9803061,0.0001772217,0.0003061216,0.0002678028,0.01828369,9.42384e-7,9.365821e-7,0.000005843549,0.0006513543],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9458107,"threshold_uncertainty_score":0.7942221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04568972929529531,"score_gpt":0.4247022349858182,"score_spread":0.3790125056905229,"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."}}