{"id":"W3150713369","doi":"10.29173/iasl7729","title":"Enhancing literacy and curriculum using digitalized collections and approaches","year":2021,"lang":"en","type":"article","venue":"IASL Annual Conference Proceedings","topic":"Library Science and Information Literacy","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Curriculum; Variety (cybernetics); Computer science; Information literacy; Subject (documents); Engineering ethics; Literacy; Curriculum development; Knowledge management; World Wide Web; Sociology; Public relations; Political science; Pedagogy; Engineering; Artificial intelligence","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":["scholarly_communication"],"category_scores_codex":[0.0002823569,0.0001124587,0.0001489776,0.0001089201,0.001040358,0.002366614,0.0001126217,0.00007081707,0.000103416],"category_scores_gemma":[0.000338935,0.000106101,0.00002682546,0.0008237562,0.0002425296,0.01604087,0.000115348,0.0001094042,0.000004197039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002410941,"about_ca_system_score_gemma":0.0003224405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001745119,"about_ca_topic_score_gemma":0.00001161406,"domain_scores_codex":[0.9989496,0.00002055238,0.0002339486,0.0002608498,0.0002493894,0.0002856215],"domain_scores_gemma":[0.9991721,0.00004233972,0.00009940886,0.0000441495,0.0004517836,0.0001902337],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.000007978459,0.00005914094,0.02783201,0.00008769571,0.00001599141,0.000002985259,0.8458346,4.689799e-7,0.0002840177,0.115968,0.000747876,0.00915917],"study_design_scores_gemma":[0.0005791092,0.00008447697,0.004327595,0.0002777078,0.00003009147,0.00008841725,0.8257541,0.006653879,0.002224132,0.01253038,0.1468042,0.0006459498],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9723656,0.000183997,0.0003145938,0.001665181,0.0001036399,0.0001462801,0.00001246646,0.00007755231,0.02513072],"genre_scores_gemma":[0.9925428,0.0001167196,0.002774595,0.0004928719,0.0001105648,0.000009906738,0.000005002275,0.000004774223,0.003942773],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1460563,"threshold_uncertainty_score":0.998669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03830748169691753,"score_gpt":0.2905301113569849,"score_spread":0.2522226296600674,"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."}}