{"id":"W1781129675","doi":"","title":"Multiliteracies and Equity: How Do Canadian Schools Measure up?.","year":2006,"lang":"en","type":"article","venue":"Education Canada","topic":"Literacy, Media, and Education","field":"Arts and Humanities","cited_by":65,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Equity (law); Measure (data warehouse); Political science; Computer science","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":[0.0001119899,0.0001223548,0.00009859998,0.0001196532,0.0004261436,0.0006973827,0.00009951521,0.00003070973,0.0007885495],"category_scores_gemma":[0.00008107493,0.0001180321,0.00001784129,0.00004688822,0.00005432002,0.0004036894,0.00001009874,0.0001113583,0.00001194487],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004771551,"about_ca_system_score_gemma":0.007440964,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9953102,"about_ca_topic_score_gemma":0.9993859,"domain_scores_codex":[0.9991524,0.00003068684,0.000138768,0.0001900725,0.0002172747,0.0002707879],"domain_scores_gemma":[0.9991895,0.00003578226,0.00005723032,0.0001786995,0.000286131,0.0002526759],"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":[0.000002103566,0.00002503534,0.006851529,0.00004053182,0.00000892067,7.972242e-7,0.07632131,5.118699e-7,0.000003884877,0.01238303,0.8771114,0.02725098],"study_design_scores_gemma":[0.0000779138,0.000005384484,0.009540262,0.00003448088,0.00001142999,0.000003093664,0.0806151,0.000007113415,0.00002386062,0.001175941,0.9083517,0.0001536925],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9319859,0.002382457,0.000001471888,0.02395035,0.01205024,0.0002558716,0.0001443748,0.00003148824,0.02919787],"genre_scores_gemma":[0.9577479,0.000007066183,0.00004086432,0.001558182,0.002599445,0.00004069982,0.0001887153,0.00001274087,0.03780434],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03124037,"threshold_uncertainty_score":0.9981859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01803104829716624,"score_gpt":0.2266137825304253,"score_spread":0.2085827342332591,"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."}}