{"id":"W7162036147","doi":"10.82308/22351","title":"Technology and motivation in higher education","year":2015,"lang":"en","type":"dissertation","venue":"","topic":"Gender and Technology in Education","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Attribution; Higher education; Causality (physics); Attribution bias; Affect (linguistics)","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":[0.001356904,0.0001513317,0.0001573513,0.0009357402,0.001339539,0.002989129,0.0002602306,0.0004756994,0.00456661],"category_scores_gemma":[0.004038925,0.00007947284,0.0002080608,0.0007409853,0.00115955,0.0006643055,0.001040741,0.0006307082,0.0002477107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001559279,"about_ca_system_score_gemma":0.001831687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004623837,"about_ca_topic_score_gemma":0.007635945,"domain_scores_codex":[0.9993266,0.0002686827,0.00002192461,0.00005633817,0.0001431731,0.0001832529],"domain_scores_gemma":[0.997612,0.0007207851,0.0005281117,0.00004526219,0.000163985,0.0009297696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001532725,0.001360977,0.7722221,0.000246312,0.00007722263,0.0002944327,0.0181683,0.0006102746,0.0008988237,0.0368204,0.003161275,0.1659865],"study_design_scores_gemma":[0.00001350084,0.0001824993,0.9680201,0.000164801,0.00001939703,0.0001222151,0.007836484,0.0004686167,0.0002170193,0.009380064,0.01355146,0.00002378923],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9651136,0.003250188,0.0003041297,0.001652561,0.00005821208,0.0000272757,0.00003115708,0.000007759723,0.02955521],"genre_scores_gemma":[0.9973375,0.0006347746,0.00009656804,0.00008598471,0.00001585302,0.000006975134,0.000008620602,0.000001853874,0.001811817],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004623837,"threshold_uncertainty_score":0.01527685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04544376707219614,"score_gpt":0.3713171683807499,"score_spread":0.3258734013085537,"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."}}