{"id":"W4387537505","doi":"10.1080/13540602.2023.2263732","title":"Algorithmic futures: an analysis of teacher professional digital competence frameworks through an algorithm literacy lens","year":2023,"lang":"en","type":"article","venue":"Teachers and Teaching","topic":"Digital literacy in education","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Competence (human resources); Literacy; Through-the-lens metering; Computer science; Perception; Computer literacy; Algorithm; Digital literacy; Mathematics education; Knowledge management; Psychology; Pedagogy; Lens (geology); Engineering; Social psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.02759417,0.0002760921,0.0004626173,0.01445848,0.004860198,0.01120909,0.001114784,0.001535219,0.003679869],"category_scores_gemma":[0.06521054,0.0004268182,0.0004704717,0.01005545,0.01205839,0.01192088,0.005993741,0.002618541,0.0003886166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02094698,"about_ca_system_score_gemma":0.02838114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02166494,"about_ca_topic_score_gemma":0.02758662,"domain_scores_codex":[0.9802237,0.009436382,0.001816198,0.001172007,0.00621114,0.001140539],"domain_scores_gemma":[0.9012936,0.0716506,0.005140903,0.003134385,0.01658875,0.002191794],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00003783237,0.0000495417,0.01662464,0.001833349,0.00001538678,0.0005654215,0.3735758,0.0005500676,0.0007166815,0.4691392,0.005622667,0.1312694],"study_design_scores_gemma":[0.00001897976,0.0001013575,0.02677572,0.004542763,0.00004858167,0.001074038,0.3938495,0.002047164,0.001824842,0.08136939,0.488272,0.00007554214],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4227546,0.02283741,0.07898886,0.03311732,0.0003247821,0.0009516149,0.0009457766,0.0003677121,0.4397119],"genre_scores_gemma":[0.9640821,0.005125907,0.02101659,0.0007271802,0.00004036599,0.0004015495,0.0003695527,0.0001307601,0.008106075],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02759417,"threshold_uncertainty_score":0.1519817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01524365169935963,"score_gpt":0.3221089414741447,"score_spread":0.3068652897747851,"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."}}