{"id":"W3047964325","doi":"10.33137/twpl.v42i1.33527","title":"Variation in subject doubling in Homeland and Heritage Faetar","year":2020,"lang":"en","type":"article","venue":"Toronto Working Papers in Linguistics","topic":"Linguistic Variation and Morphology","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada; University at Buffalo; University of Toronto","keywords":"Homeland; Variety (cybernetics); Subject (documents); Variation (astronomy); Heritage language; Geography; History; Psychology; Political science; Computer science; Artificial intelligence; Library science; Law; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.000936671,0.0002277992,0.0002663704,0.001398026,0.0008816957,0.0009829224,0.0002037853,0.0002200849,0.004925838],"category_scores_gemma":[0.002671998,0.0001261659,0.0001005731,0.000745476,0.001454253,0.0006307095,0.0009692306,0.0002860345,0.00032807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005119511,"about_ca_system_score_gemma":0.000340781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01380759,"about_ca_topic_score_gemma":0.03879862,"domain_scores_codex":[0.9994637,0.0001040327,0.00003861037,0.0001653923,0.0001468888,0.00008135421],"domain_scores_gemma":[0.9983767,0.00067154,0.0003658351,0.0002530593,0.0002323355,0.0001004274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0006469496,0.00008330837,0.66012,0.000152037,0.00008486629,0.003681098,0.1810909,0.0001713887,0.05357852,0.007827273,0.0009568104,0.09160686],"study_design_scores_gemma":[0.0000114853,0.0001296338,0.9590977,0.00003801432,0.0000263563,0.004026348,0.02531084,0.0001637628,0.002613317,0.001295901,0.007242918,0.0000436803],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959871,0.00007580465,0.000160422,0.00002143873,0.000003682519,0.000002828859,0.00006917229,0.000005436475,0.003674014],"genre_scores_gemma":[0.9988046,0.00004066285,0.0001928158,0.00001002761,0.000002954982,0.000002732438,0.00007926604,0.000008655558,0.0008583423],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01380759,"threshold_uncertainty_score":0.02745444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03098202573051735,"score_gpt":0.2901882915996553,"score_spread":0.259206265869138,"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."}}