{"id":"W2110869378","doi":"10.1007/s11165-012-9324-z","title":"Data Generation in the Discovery Sciences—Learning from the Practices in an Advanced Research Laboratory","year":2012,"lang":"en","type":"article","venue":"Research in Science Education","topic":"Statistics Education and Methodologies","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Scientific literacy; Inclusion (mineral); Science education; Curriculum; Relation (database); Engineering ethics; Process (computing); Scientific misconceptions; Nature of Science; Natural (archaeology); Scientific discovery; Epistemology; Phenomenon; Next Generation Science Standards; Data science; Ethnography; Mathematics education; Sociology; Computer science; Psychology; Social science; Pedagogy; Cognitive science; Engineering; Geography","routes":{"ca_aff":true,"ca_fund":false,"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":["metaresearch"],"category_scores_codex":[0.1197176,0.00007439809,0.0000922523,0.0006963395,0.0008434833,0.0007903103,0.002283428,0.00004589824,0.00002621801],"category_scores_gemma":[0.1183207,0.00004664142,0.000005739424,0.005840679,0.001436616,0.00430369,0.0003306594,0.001043198,0.00001605917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003734401,"about_ca_system_score_gemma":0.005449107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001491352,"about_ca_topic_score_gemma":0.00349508,"domain_scores_codex":[0.9890037,0.00753074,0.0003096722,0.0005333968,0.001882515,0.0007399967],"domain_scores_gemma":[0.9746467,0.0230442,0.0002088105,0.001380455,0.0006188338,0.0001009994],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.00005323721,0.00276927,0.4806548,0.0000501769,0.000002493905,0.000001894255,0.1164932,0.0003022863,0.03255471,0.2872922,0.02369008,0.05613568],"study_design_scores_gemma":[0.0002439705,0.0001304044,0.4232422,0.0001546645,0.000003079767,0.00000256944,0.4511861,0.008537679,0.001588247,0.1016703,0.0130261,0.0002146619],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9911444,0.0005279689,0.0002279687,0.005682118,0.0006722023,0.0006015004,0.0000129721,0.00000733254,0.001123525],"genre_scores_gemma":[0.9758753,0.0001983396,0.02303433,0.00009358567,0.0004760901,0.0001665832,0.0000418136,0.000006522196,0.0001074962],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.334693,"threshold_uncertainty_score":0.9666482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9211112676398054,"score_gpt":0.7257521554171125,"score_spread":0.195359112222693,"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."}}