{"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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007396606,0.0000600128,0.0001481618,0.0001731218,0.0001374975,0.000256336,0.0001217573,0.00003974635,0.00004463085],"category_scores_gemma":[0.001886284,0.00005203118,0.00002744911,0.0003497315,0.0001476087,0.03729699,0.00002147513,0.0001399462,0.00001162475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004147007,"about_ca_system_score_gemma":0.0007992018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007653294,"about_ca_topic_score_gemma":0.000001123535,"domain_scores_codex":[0.9988422,0.0001477417,0.000607741,0.00004969071,0.0002555924,0.00009697995],"domain_scores_gemma":[0.9988804,0.0001902483,0.0002754825,0.00004919407,0.0004356725,0.0001689851],"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":[0.00006265343,0.00008421259,0.01457782,0.0001225257,0.00001295943,0.000101288,0.7710026,0.000004566702,0.000005557035,0.05982465,0.07918061,0.0750206],"study_design_scores_gemma":[0.0001846773,0.0001131025,0.0005637077,0.00008647947,0.000003459395,0.0001827236,0.1073833,0.0000920349,0.00001554014,0.002496815,0.8888044,0.00007376308],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7449404,0.01601225,0.0001576371,0.2170901,0.002367148,0.0003079469,0.00000534509,0.00002800842,0.01909113],"genre_scores_gemma":[0.9888569,0.0008151509,0.001438451,0.008227152,0.0005490673,0.000002117369,0.000003099314,0.000001848582,0.000106215],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8096238,"threshold_uncertainty_score":0.9761679,"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."}}