{"id":"W7030036867","doi":"","title":"Maybe She'll Say Yes: How Young Learners Acquire and Apply Knowledge about Inconsistent Causal Relationships from Different Domains","year":2025,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Innovative Educational Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Waterloo; Social Sciences and Humanities Research Council of Canada; Jacobs Foundation","keywords":"Cognition; Causal reasoning; Probabilistic logic; Causality (physics); Causal model; Causal inference; Knowledge level; Cognitive development","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000238925,0.0004467909,0.0003784772,0.0005181442,0.0004975552,0.003079653,0.001368353,0.0002960133,0.00002585594],"category_scores_gemma":[0.0007674443,0.0003974164,0.0001215111,0.001265909,0.0003822845,0.004612715,0.001480976,0.0009059345,0.0002926356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001656526,"about_ca_system_score_gemma":0.0003683352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006429806,"about_ca_topic_score_gemma":0.000008139618,"domain_scores_codex":[0.9975288,0.0001946824,0.0004869621,0.0009096106,0.0003607583,0.000519187],"domain_scores_gemma":[0.9979237,0.0007455624,0.0001921328,0.000860503,0.0001061376,0.0001719398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00002817677,0.0002861266,0.4569582,0.00006044336,0.0001462584,0.00001722704,0.0004650901,0.000002317332,0.0002418096,0.5199476,0.01003501,0.01181176],"study_design_scores_gemma":[0.001061852,0.0000991932,0.5836979,0.0004392696,0.00004006787,0.00001648044,0.001665116,0.00067228,0.004498484,0.2958792,0.1108339,0.001096276],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9222102,0.001394712,0.02497174,0.01736129,0.000867204,0.000643992,0.0009334524,0.001535706,0.03008171],"genre_scores_gemma":[0.9877561,0.00003720508,0.007438761,0.0004961146,0.0001054316,0.00009505847,0.0003934307,0.00003910809,0.003638772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2240684,"threshold_uncertainty_score":0.9998478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.020486277081102,"score_gpt":0.2381446924960357,"score_spread":0.2176584154149337,"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."}}