{"id":"W2059642274","doi":"10.1007/s10649-008-9154-0","title":"Investigating imagination as a cognitive space for learning mathematics","year":2008,"lang":"en","type":"article","venue":"Educational Studies in Mathematics","topic":"Education Methods and Practices","field":"Social Sciences","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"Wilfrid Laurier University","funders":"Ministère de l’Éducation, Gouvernement de l’Ontario","keywords":"Mathematics education; Space (punctuation); Cognition; Root (linguistics); Square root; Numerical cognition; Work (physics); Mathematics; Psychology; Pedagogy; Epistemology; Computer science; Geometry; Linguistics; Philosophy; Engineering","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":[0.002429334,0.0001254457,0.0002240064,0.000118934,0.001164644,0.00004667234,0.0001410815,0.00005614767,0.0001293118],"category_scores_gemma":[0.04535194,0.0001268036,0.00005092828,0.0004083963,0.0005814952,0.0003312267,0.000033868,0.0001843341,0.00005594224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001933177,"about_ca_system_score_gemma":0.0006404653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001159243,"about_ca_topic_score_gemma":0.00009193326,"domain_scores_codex":[0.9985783,0.0002300492,0.0003661428,0.0001900358,0.0003902398,0.0002452153],"domain_scores_gemma":[0.9876439,0.01116491,0.0003801306,0.00008181667,0.0006588511,0.00007041961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000001937704,0.000365433,0.006641715,0.000236467,0.00005365167,4.500598e-7,0.5879464,0.00002247887,0.00002884646,0.4017053,0.002302331,0.0006950389],"study_design_scores_gemma":[0.0002093458,0.00003034037,0.0008376955,0.0002713726,0.00003442777,0.000009598165,0.4581758,0.0002653563,0.00007974511,0.5314069,0.008487695,0.0001917146],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8219073,0.0034493,0.008687889,0.04330108,0.001701612,0.00182194,0.000008427982,0.0001191003,0.1190033],"genre_scores_gemma":[0.2600361,0.001761753,0.7197992,0.0002600839,0.0009426133,0.0006059536,0.00001722092,0.00003249033,0.0165446],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7111112,"threshold_uncertainty_score":0.9626895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2776992231602562,"score_gpt":0.542585895566123,"score_spread":0.2648866724058668,"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."}}