{"id":"W2727116440","doi":"","title":"INVESTIGATING LANGUAGE POLICY IN ONTARIO: GLOBALIZED INFLUENCES AND LOCAL PRIORITIES","year":2017,"lang":"en","type":"article","venue":"QSpace (Queen's University Library)","topic":"Multilingual Education and Policy","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Political science; Linguistics; Business","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":[],"consensus_categories":[],"category_scores_codex":[0.00009841386,0.0001031615,0.0001449026,0.0001686037,0.0008775169,0.0002881685,0.0004193881,0.0001066315,0.0002090682],"category_scores_gemma":[0.0003154045,0.0001188562,0.00003317867,0.0001592277,0.0008829425,0.001583119,0.0001908465,0.0001880271,0.00001135534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000198945,"about_ca_system_score_gemma":0.001700684,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9725353,"about_ca_topic_score_gemma":0.5553201,"domain_scores_codex":[0.9991166,0.0001545684,0.00008578269,0.0001974144,0.0001667821,0.0002788639],"domain_scores_gemma":[0.9993367,0.00008318327,0.000116453,0.0002096544,0.000017842,0.0002362045],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001895601,0.00002037942,0.7638438,0.00001674234,0.000009309457,0.00002297792,0.131724,0.000004251371,0.000003979387,0.09780313,0.004093309,0.002439182],"study_design_scores_gemma":[0.0004366604,0.00001471125,0.6358914,0.00006015543,0.000006166138,9.930315e-8,0.05162067,0.000002983832,0.0001101455,0.0009376982,0.3107284,0.0001908236],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.827484,0.000008137795,0.000002053474,0.07459942,0.00009792084,0.0001103674,0.000006338004,0.00007099208,0.09762079],"genre_scores_gemma":[0.8795973,0.00006932777,0.0007135718,0.0006796845,0.00009756505,3.063407e-7,0.000002703188,0.00000555061,0.118834],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4172152,"threshold_uncertainty_score":0.6749239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02353574616114999,"score_gpt":0.319406929830062,"score_spread":0.295871183668912,"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."}}