{"id":"W2791615606","doi":"10.52041/serj.v16i2.189","title":"PROMOTING MODELING AND COVARIATIONAL REASONING AMONG SECONDARY SCHOOL STUDENTS IN THE CONTEXT OF BIG DATA","year":2017,"lang":"en","type":"article","venue":"Statistics Education Research Journal","topic":"Statistics Education and Methodologies","field":"Mathematics","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Division of Mathematical Sciences; University of Toronto; Fields Institute for Research in Mathematical Sciences","keywords":"Mathematics education; Context (archaeology); Big data; Computer science; Visualization; Data visualization; Data science; Psychology; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01514242,0.0001072342,0.0002032981,0.0002832662,0.001028618,0.0008535034,0.001427333,0.0000573369,0.00009072644],"category_scores_gemma":[0.07596637,0.00008458384,0.00001497605,0.0001151817,0.0003235415,0.0002971659,0.0003913491,0.0009886645,0.000003190185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009431852,"about_ca_system_score_gemma":0.002382443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000304854,"about_ca_topic_score_gemma":0.0002248466,"domain_scores_codex":[0.996691,0.001160367,0.0005865203,0.0002405636,0.001026428,0.0002951348],"domain_scores_gemma":[0.993489,0.003918058,0.0005256539,0.0007956569,0.001123653,0.0001479528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001001269,0.00108431,0.2335747,0.0005193431,0.0001538038,0.00002179163,0.03634461,0.00001020578,0.00009779308,0.4411279,0.07938886,0.2075766],"study_design_scores_gemma":[0.0009138754,0.00008445609,0.2136085,0.0005248333,0.00004275659,0.0001041172,0.0470175,0.01688554,0.00002378951,0.7199306,0.0006771996,0.0001868207],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7136731,0.0005405248,0.2753007,0.002844419,0.002719918,0.00123048,0.0009392136,0.00001507388,0.002736495],"genre_scores_gemma":[0.8074855,0.0002476162,0.1913951,0.00005523924,0.0004066715,0.0000278455,0.0000673925,0.00001635906,0.0002983799],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2788026,"threshold_uncertainty_score":0.9318172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4994339851688935,"score_gpt":0.5728007331029144,"score_spread":0.07336674793402082,"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."}}