{"id":"W2782534858","doi":"10.29173/iasl7472","title":"Motivation to transfer learning to multiple contexts","year":2021,"lang":"en","type":"article","venue":"IASL Annual Conference Proceedings","topic":"Online and Blended Learning","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Information literacy; Transfer of learning; Process (computing); Set (abstract data type); Computer science; Knowledge management; Mathematics education; Sanctions; Work (physics); Psychology; Pedagogy; Artificial intelligence; Political science; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004926611,0.0001452556,0.000199241,0.0001101585,0.0005161628,0.0003090597,0.0002190202,0.0001179642,0.0003932493],"category_scores_gemma":[0.003243542,0.0001546467,0.00005689437,0.0007012209,0.00005098069,0.0005414566,0.00006561215,0.0003214417,0.000227249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000649565,"about_ca_system_score_gemma":0.0004399734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002262461,"about_ca_topic_score_gemma":0.0005396406,"domain_scores_codex":[0.998392,0.00004981763,0.0002174907,0.0004188355,0.0004488361,0.0004730501],"domain_scores_gemma":[0.9978761,0.0001114208,0.00003110176,0.00005244715,0.001599606,0.0003293419],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006975164,0.00009940482,0.06828538,0.00003412751,0.00002307543,0.00001092121,0.7613914,0.00007321234,0.009672132,0.009839197,0.00204167,0.1484597],"study_design_scores_gemma":[0.0003517348,0.0001985534,0.01300234,0.0001406957,0.00001403241,0.000002381583,0.4072563,0.00009290175,0.00565399,0.0003754691,0.5725305,0.0003810998],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9760394,0.00001820179,0.00168315,0.01143746,0.000131911,0.0002546483,0.00000706608,0.0001897756,0.01023836],"genre_scores_gemma":[0.9886529,0.00001411966,0.001779381,0.0008111313,0.0003305086,0.00002970249,0.000007699709,0.00001617941,0.00835835],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5704889,"threshold_uncertainty_score":0.630631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03080269190109307,"score_gpt":0.3070393191372028,"score_spread":0.2762366272361097,"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."}}