{"id":"W2394346711","doi":"","title":"Research and Inspiration of Adjustment of School Distribution in Canada:A Case Study of Saskatchewan","year":2007,"lang":"en","type":"article","venue":"Waiguo jiaoyu yanjiu","topic":"Education Systems and Policy","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Distribution (mathematics); Mathematics education; Psychology; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002369901,0.00004834674,0.0001570295,0.0001256251,0.00009173201,0.000005729856,0.00007998029,0.00004612529,0.00003005248],"category_scores_gemma":[0.0001626335,0.00004750726,0.00001119651,0.0005900136,0.0001089134,0.00007705139,0.00002325066,0.00009524681,4.921653e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00046179,"about_ca_system_score_gemma":0.002171373,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9966649,"about_ca_topic_score_gemma":0.9978339,"domain_scores_codex":[0.9985166,0.0002555556,0.0004399567,0.0001137449,0.0004863676,0.0001877712],"domain_scores_gemma":[0.9991062,0.0001653559,0.0001634311,0.000143604,0.0003164558,0.0001049383],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.00003169543,0.000562,0.8055366,0.00007931754,0.0000160835,0.00002351262,0.1838315,0.00001276043,0.0005689127,0.003252039,0.0004628831,0.005622752],"study_design_scores_gemma":[0.0002864593,0.0001520563,0.319263,0.00002831777,0.000003628739,0.000002610347,0.6790444,0.000002335729,0.0006655891,0.00005276738,0.0004610888,0.00003777237],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983101,0.0001091365,0.000008017911,0.0001297929,0.0000974525,0.0004427636,0.00002431542,0.000002318095,0.000876065],"genre_scores_gemma":[0.9997434,0.00000967232,0.00001091245,0.000007195091,0.00008007007,0.000007680562,0.000004398271,0.000003080056,0.0001336159],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.495213,"threshold_uncertainty_score":0.3851923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0942887133124464,"score_gpt":0.4164206577551542,"score_spread":0.3221319444427078,"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."}}