{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005921261,0.0004306431,0.0004766297,0.001114807,0.001520457,0.002857519,0.0007202022,0.0013834,0.004519715],"category_scores_gemma":[0.002976971,0.0004399105,0.00102371,0.001320471,0.0008724827,0.0008941896,0.001824485,0.001738549,0.001385495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008926241,"about_ca_system_score_gemma":0.001747746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04559662,"about_ca_topic_score_gemma":0.06684008,"domain_scores_codex":[0.9990873,0.0001162583,0.00006938178,0.0001215576,0.0002088045,0.0003966315],"domain_scores_gemma":[0.996973,0.0005654647,0.0008777298,0.00009511277,0.0003495249,0.001139194],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001008636,0.0002288757,0.9938859,0.00001477238,0.0000454345,0.0001825779,0.002372157,0.00002809662,0.0002130975,0.0000588796,0.0003725805,0.002496651],"study_design_scores_gemma":[0.00000319774,0.0000811309,0.9917018,0.00001698631,0.00004075695,0.00005276088,0.007545738,0.00008213564,0.00009052981,0.00003196894,0.0003414136,0.00001151307],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989988,0.00004501027,0.00001047644,0.0001028051,0.00001222423,0.000002141845,0.00009797949,0.000001519045,0.0007289879],"genre_scores_gemma":[0.9990039,0.00004745767,0.00001692585,0.00003189592,0.000006791262,0.000005958146,0.0001585404,0.000002200513,0.000726287],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04559662,"threshold_uncertainty_score":0.09066248,"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."}}