{"id":"W2741743121","doi":"","title":"'Toronto has everything', 'Toronto's got it all': Ethnolinguistic Dimensions of have in Toronto English","year":2014,"lang":"en","type":"article","venue":"","topic":"Linguistic Variation and Morphology","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Media studies; Sociology; History","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":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00171121,0.0001754041,0.0003691298,0.00002346542,0.0002418301,0.00005355095,0.000371781,0.0002841003,0.006738776],"category_scores_gemma":[0.01097791,0.0001541634,0.00009569465,0.00004922681,0.0002564795,0.0002178916,0.00009816491,0.0001190383,0.00002333879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001228539,"about_ca_system_score_gemma":0.0002752863,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7001163,"about_ca_topic_score_gemma":0.9330544,"domain_scores_codex":[0.9977311,0.0004141553,0.000541988,0.0003732941,0.000438057,0.0005013524],"domain_scores_gemma":[0.998162,0.0005715239,0.000185588,0.0003736269,0.0004866891,0.0002206217],"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":[0.00003864752,0.0003199372,0.003052738,0.00002064223,0.00005596942,0.000009596848,0.2281211,0.00002344288,0.0001900166,0.747682,0.01830993,0.00217607],"study_design_scores_gemma":[0.001202121,0.0001459013,0.003960756,0.00004871366,0.00006024027,9.459671e-7,0.0542661,0.0007778564,0.00008509508,0.004001945,0.9350203,0.0004299958],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.005694643,0.0004189603,0.0010647,0.001071718,0.003764142,0.0003549627,0.00000836885,0.0001695548,0.9874529],"genre_scores_gemma":[0.9885252,0.0001898702,0.002442679,0.00134287,0.0009902816,0.00001708283,0.00001089408,0.00001844156,0.006462683],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9828305,"threshold_uncertainty_score":0.997353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03637241351257126,"score_gpt":0.3388558905922758,"score_spread":0.3024834770797046,"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."}}