{"id":"W3040529931","doi":"10.20355/jcie29414","title":"Case Studies, Cuts, and Critical Information Literacy","year":2020,"lang":"en","type":"article","venue":"Journal of Contemporary Issues in Education","topic":"Library Science and Information Literacy","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Information literacy; Library instruction; Task (project management); Government (linguistics); Face (sociological concept); Literacy; Transferable skills analysis; Critical thinking; Mathematics education; Higher education; Computer science; Pedagogy; Political science; Sociology; Psychology; Management; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007866972,0.0005799707,0.0003176762,0.001940621,0.01260278,0.005431555,0.00235883,0.004383004,0.005416339],"category_scores_gemma":[0.01695814,0.0003875757,0.0004063852,0.003201287,0.008727693,0.005068023,0.006459883,0.004302976,0.0006629365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005398601,"about_ca_system_score_gemma":0.002964693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00442085,"about_ca_topic_score_gemma":0.01413887,"domain_scores_codex":[0.9897377,0.008067775,0.0003321182,0.0002434792,0.0007719092,0.000847045],"domain_scores_gemma":[0.9791334,0.01678837,0.001074494,0.0008952995,0.0009166869,0.001191637],"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":[0.0002008351,0.001783049,0.01315281,0.001041838,0.00002870649,0.05511518,0.5191615,0.001196611,0.001200981,0.2606235,0.03647128,0.1100238],"study_design_scores_gemma":[0.00006540705,0.0003921285,0.005074888,0.001669563,0.00002969618,0.02786871,0.4643668,0.0007990035,0.002802142,0.04953831,0.4473362,0.00005714723],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5946761,0.01147492,0.01680997,0.03261526,0.0005970906,0.001176068,0.0001438881,0.0001813734,0.3423254],"genre_scores_gemma":[0.9357713,0.00610731,0.01831217,0.00285777,0.0001452582,0.0007071825,0.00008869577,0.00004600067,0.03596438],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01260278,"threshold_uncertainty_score":0.04160506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05089333578644546,"score_gpt":0.4116649431107904,"score_spread":0.3607716073243449,"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."}}