{"id":"W2788365048","doi":"","title":"When Technology Does Not Add Up: ICT Use Negatively Predicts Mathematics and Science Achievement for Finnish and Turkish Students in PISA 2012","year":2017,"lang":"en","type":"article","venue":"Journal of educational multimedia and hypermedia","topic":"Gender and Technology in Education","field":"Social Sciences","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Turkish; Information and Communications Technology; Mathematics education; Academic achievement; Technology integration; Educational technology; Technological literacy; Psychology; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.001003668,0.0001077637,0.0002068219,0.0003858104,0.0007122937,0.0002610776,0.0004421101,0.0001371681,0.00002519367],"category_scores_gemma":[0.004142764,0.00008507497,0.00002171967,0.00008994111,0.001932896,0.0009533853,0.0001142549,0.0002025513,8.253675e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007434082,"about_ca_system_score_gemma":0.0006036865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000105111,"about_ca_topic_score_gemma":0.0005488899,"domain_scores_codex":[0.9988144,0.00002175333,0.0003178115,0.0001980425,0.0003956072,0.0002523921],"domain_scores_gemma":[0.9983062,0.0006403169,0.0003975267,0.0001441844,0.0003416517,0.000170168],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002630389,0.0004133978,0.9432212,0.00003261682,0.00003862769,0.000001056005,0.03674515,1.627286e-7,0.001396864,0.005848708,0.001043348,0.01123256],"study_design_scores_gemma":[0.0009775107,0.00009360298,0.94881,0.00008684534,0.00004464019,0.00001642397,0.01262135,0.00003957248,0.0003779334,0.03463486,0.002176657,0.000120624],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9758958,0.0002973192,0.00001629606,0.02220319,0.001164275,0.0003215225,0.00001904932,0.000005627752,0.00007693317],"genre_scores_gemma":[0.9801186,0.0005876718,0.01835293,0.0001030659,0.0002905172,0.00003437655,0.000002026548,0.000006700283,0.0005040676],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02878615,"threshold_uncertainty_score":0.7121837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03995764111710965,"score_gpt":0.3662974960720645,"score_spread":0.3263398549549548,"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."}}