{"id":"W3152386218","doi":"10.29173/iasl8011","title":"A Learning Object Approach for Designing Information Literacy Instructional Materials","year":2021,"lang":"en","type":"article","venue":"IASL Annual Conference Proceedings","topic":"Open Education and E-Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Learning object; Computer science; Personalization; Interoperability; Context (archaeology); Flexibility (engineering); Object (grammar); Instructional design; Relation (database); Information literacy; Multimedia; Literacy; Affordance; Human–computer interaction; World Wide Web; Artificial intelligence; Pedagogy","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005394824,0.0001759391,0.0002109046,0.0001548006,0.0004265428,0.002442619,0.0004388005,0.00009639191,0.00007758144],"category_scores_gemma":[0.000642021,0.0001807987,0.00005393302,0.0004512781,0.0000329448,0.005982228,0.0002118802,0.0002561894,0.00004618767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005123606,"about_ca_system_score_gemma":0.0003870054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009829968,"about_ca_topic_score_gemma":1.000415e-7,"domain_scores_codex":[0.9986116,0.00003479557,0.0003711528,0.0003559497,0.0002879634,0.0003384966],"domain_scores_gemma":[0.9974661,0.00005616591,0.0002440882,0.00008714396,0.002037749,0.0001087635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.000105049,0.0001920479,0.003665392,0.0007038238,0.00009355029,0.000003005934,0.6226429,0.0002221455,0.01523441,0.2180361,0.002614905,0.1364867],"study_design_scores_gemma":[0.00344377,0.001087269,0.01182442,0.0005237301,0.00006950656,0.001097699,0.5839521,0.1367562,0.1608621,0.009269878,0.08855707,0.002556174],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4759981,0.00001952469,0.5097816,0.0008356171,0.0005846639,0.0004157737,0.000009817504,0.0003905206,0.01196438],"genre_scores_gemma":[0.8020112,0.000006825807,0.196727,0.0002329614,0.000129401,0.0001056004,0.00007641698,0.000007967853,0.0007026475],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3260131,"threshold_uncertainty_score":0.9985929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02067234385049974,"score_gpt":0.2669105980799151,"score_spread":0.2462382542294154,"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."}}