{"id":"W7092358468","doi":"10.5281/zenodo.17382601","title":"Linguistic Landscapes in Toronto: Public Signs and Urban Multilingualism (2025)","year":2025,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Multilingual Education and Policy","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Linguistic landscape; Multilingualism; Linguistic diversity; Visual language; Diversity (politics); Qualitative research; Cultural diversity","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005784638,0.001510271,0.0008799948,0.004151177,0.001842541,0.003020227,0.001997032,0.001163004,0.02924575],"category_scores_gemma":[0.003211805,0.0005477512,0.0008093675,0.01000397,0.0007391428,0.000841002,0.002401355,0.001182284,0.01815533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0111122,"about_ca_system_score_gemma":0.009525909,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8534908,"about_ca_topic_score_gemma":0.9368106,"domain_scores_codex":[0.9992809,0.0000848865,0.00005705481,0.0001336048,0.0002403562,0.0002031952],"domain_scores_gemma":[0.9982452,0.0002677814,0.0001517596,0.0002704389,0.0007553456,0.00030952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006142739,0.00001920924,0.003247271,0.00082229,0.0000304588,0.00009017503,0.000393858,0.000384561,0.0001174718,0.0007527364,0.9894399,0.00464064],"study_design_scores_gemma":[0.00009101363,0.00001100794,0.03647618,0.0006543795,0.00003306792,0.00008430761,0.001362343,0.0005359849,0.0002724501,0.0005101085,0.9599234,0.00004584315],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001019854,0.0001960802,0.00004660444,0.00007152605,0.00001834103,0.00002332109,0.9971663,0.0001251146,0.001332814],"genre_scores_gemma":[0.002115378,0.0001300787,0.0002055733,0.00002161669,0.000004668774,0.00008930069,0.9962595,0.00002805379,0.001145967],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1465092,"threshold_uncertainty_score":0.2947441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06461168085135303,"score_gpt":0.3986687677787997,"score_spread":0.3340570869274467,"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."}}