{"id":"W7084158795","doi":"10.64628/aam.4xsuwewwf","title":"Thunder Bay: Local news is important for conversations on reconciliation","year":2019,"lang":"en","type":"article","venue":"","topic":"Data Analysis with R","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Thunder; Narrative; Agency (philosophy); Conversation","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.03163513,0.00108864,0.001560492,0.003050615,0.01108701,0.01420277,0.002806745,0.0139728,0.1182208],"category_scores_gemma":[0.1366198,0.0009599833,0.001316855,0.002835172,0.00643142,0.01044028,0.008043073,0.02940459,0.04248715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004801003,"about_ca_system_score_gemma":0.009138974,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0117843,"about_ca_topic_score_gemma":0.02828474,"domain_scores_codex":[0.9839376,0.007594655,0.0008470919,0.001315204,0.004747289,0.001558152],"domain_scores_gemma":[0.8692913,0.05935481,0.005184991,0.0132613,0.03366948,0.01923815],"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":[0.00005358304,0.00001205921,0.0002379755,0.00003850776,0.000009132363,0.0001291826,0.0005087106,0.0000151661,0.0001242963,0.001846372,0.9906307,0.006394342],"study_design_scores_gemma":[0.00002619493,0.00002058989,0.0008494993,0.0002012752,0.00001585657,0.00011159,0.002181116,0.00009038748,0.0002462461,0.003421106,0.9927947,0.00004139743],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.0008759391,0.001695633,0.002172597,0.9078015,0.06858865,0.00003991121,0.0006739156,0.0008685177,0.01728334],"genre_scores_gemma":[0.02932004,0.002063753,0.008002155,0.6894853,0.0675505,0.0002813042,0.002466822,0.00795669,0.1928734],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9882157,"threshold_uncertainty_score":0.3954881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0218802856852804,"score_gpt":0.2601400238965847,"score_spread":0.2382597382113043,"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."}}