{"id":"W7006390221","doi":"","title":"TÜRKİYE MEB VE ONTARIO (KANADA) EYALETİ FEN VE TEKNOLOJİ DERSİ ÖĞRETİM PROGRAMLARININ KARŞILAŞTIRILARAK DEĞERLENDİRİLMESİ","year":2014,"lang":"tr","type":"article","venue":"DergiPark (Istanbul University)","topic":"Science Education and Pedagogy","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Data collection; Natural (archaeology); Work (physics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004542771,0.0003097715,0.0002121546,0.0004970166,0.003447411,0.00163611,0.0004643495,0.0005620913,0.02381476],"category_scores_gemma":[0.001123133,0.0001813731,0.0002059567,0.001034538,0.0008241636,0.0004311592,0.00118557,0.0006818342,0.001890804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0112944,"about_ca_system_score_gemma":0.03979052,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7287611,"about_ca_topic_score_gemma":0.9085432,"domain_scores_codex":[0.9995556,0.00004029797,0.00001584077,0.00005179749,0.0001372996,0.0001991591],"domain_scores_gemma":[0.9990348,0.00007301436,0.00007537959,0.00003427971,0.0005023349,0.0002802963],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008437707,0.0005098174,0.158959,0.001306124,0.00008867616,0.002829675,0.02783027,0.001166775,0.01205058,0.03496446,0.1740408,0.5854099],"study_design_scores_gemma":[0.00005398348,0.0001541035,0.2972029,0.0005233923,0.00008895034,0.0006391205,0.02779509,0.0005190244,0.003250748,0.001613204,0.6681041,0.00005540244],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6863278,0.006768015,0.002790742,0.03044601,0.0006785229,0.0003015825,0.003352948,0.000375468,0.2689589],"genre_scores_gemma":[0.6739565,0.004062623,0.007422998,0.001423916,0.00004450877,0.0001674339,0.001511825,0.0001031178,0.3113071],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2712389,"threshold_uncertainty_score":0.5456725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02984476549752834,"score_gpt":0.2627759714057971,"score_spread":0.2329312059082688,"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."}}