{"id":"W4238680365","doi":"10.32920/ryerson.14639397.v1","title":"Case studies, cuts, and critical information literacy","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Library Science and Information Literacy","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Information literacy; Library instruction; Task (project management); Critical thinking; Government (linguistics); Literacy; Mathematics education; Face (sociological concept); Transferable skills analysis; Critical literacy; Higher education; Computer science; Political science; Pedagogy; Sociology; Psychology; Management; Economics; Social 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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.008713988,0.0005368436,0.0003100439,0.002111931,0.01118157,0.00514834,0.002132986,0.003553301,0.005709869],"category_scores_gemma":[0.01485762,0.0003534767,0.0003823411,0.00308733,0.009613624,0.004732574,0.006420031,0.003495369,0.0005970192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00597485,"about_ca_system_score_gemma":0.003494839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003681463,"about_ca_topic_score_gemma":0.00981116,"domain_scores_codex":[0.9881939,0.009491985,0.0003528522,0.0002991843,0.0009157039,0.0007464433],"domain_scores_gemma":[0.973541,0.02184787,0.001292344,0.001127212,0.001028826,0.001162791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001872402,0.001495081,0.01213306,0.0008619127,0.00002557141,0.03286015,0.4536628,0.001249534,0.001358368,0.3838622,0.02247999,0.08982427],"study_design_scores_gemma":[0.0000811301,0.0004023095,0.004890563,0.001584436,0.00003042808,0.0170603,0.4403726,0.001080918,0.003596199,0.08913673,0.441708,0.00005634948],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5674402,0.008424369,0.02513825,0.02269096,0.0003780013,0.00141507,0.0001430782,0.0001379764,0.374232],"genre_scores_gemma":[0.9368674,0.004320682,0.02341796,0.001569587,0.00008538771,0.000809392,0.00009263392,0.00003529553,0.03280159],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9948516,"threshold_uncertainty_score":0.04608452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04433426217957962,"score_gpt":0.3882583913135217,"score_spread":0.3439241291339421,"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."}}