{"id":"W4388160422","doi":"10.1007/s11858-023-01534-y","title":"Technology maker practices in mathematics learning in STEM contexts: a case in Brazil and two cases in Canada","year":2023,"lang":"en","type":"article","venue":"ZDM","topic":"Teaching and Learning Programming","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Tech University; Western University","funders":"Social Sciences and Humanities Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Mathematics education; Situated; Critical thinking; Best practice; Psychology; Pedagogy; Computer science; Political science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009432731,0.00009444421,0.0001850022,0.0005238747,0.00004570943,0.0000730793,0.000188993,0.00005141826,0.000001221233],"category_scores_gemma":[0.0007490018,0.00009654983,0.00000739079,0.001214825,0.00002295138,0.000193712,0.0001767696,0.0006662972,0.000004427106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001853761,"about_ca_system_score_gemma":0.0002395082,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7009906,"about_ca_topic_score_gemma":0.9741455,"domain_scores_codex":[0.9988578,0.0001852753,0.0002585748,0.0002714061,0.0001091072,0.0003178588],"domain_scores_gemma":[0.9989688,0.0006711499,0.0001454114,0.0001749873,0.000009247889,0.00003040803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000463072,0.0000479701,0.6163508,0.00006792706,0.000003181,0.03287501,0.01110021,0.002953346,0.00001714917,0.001534547,0.00002811851,0.3350171],"study_design_scores_gemma":[0.00683156,0.0004190896,0.1602952,0.002423578,0.00001196288,0.01726419,0.1219282,0.6396706,0.0001205798,0.001526218,0.04793284,0.001575894],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997766,0.0002762805,0.0002195389,0.001404827,0.00005241466,0.0001138033,3.02751e-7,0.0001071657,0.00005969637],"genre_scores_gemma":[0.996455,0.000009386537,0.003309111,0.00004170689,0.000004886499,0.00002190755,4.629072e-7,0.000008235484,0.0001493635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6367173,"threshold_uncertainty_score":0.3937188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03422631619762192,"score_gpt":0.3147920816335792,"score_spread":0.2805657654359573,"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."}}