{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007961889,0.0002791029,0.0004126912,0.0004384753,0.0003560462,0.0008889036,0.000863599,0.0002114791,0.00001676286],"category_scores_gemma":[0.0001188485,0.0002482898,0.0001426152,0.000814128,0.0001133526,0.008042478,0.0002858531,0.0009645631,0.000009821581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006150537,"about_ca_system_score_gemma":0.00007256155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001874804,"about_ca_topic_score_gemma":0.000007824974,"domain_scores_codex":[0.9975896,0.0002519313,0.0004763798,0.0007513585,0.0004923797,0.0004382791],"domain_scores_gemma":[0.9984465,0.0002252466,0.0002323485,0.000828015,0.00009416018,0.0001737438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004446415,0.0006020997,0.01258733,0.00001414318,0.0002294201,0.00001156122,0.1076205,0.0002431765,0.00007262886,0.03808651,0.0001936697,0.8403345],"study_design_scores_gemma":[0.0004202992,0.000317263,0.1347103,0.0001607485,0.0002008,0.00002310689,0.01184229,0.8272875,0.00003016396,0.01261084,0.01157642,0.0008202716],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8394325,0.0001034267,0.156158,0.000484584,0.0005529804,0.0002007673,0.00005687313,0.0004965849,0.002514309],"genre_scores_gemma":[0.9006037,0.000009743006,0.09796797,0.0002241101,0.000226238,0.00001958816,0.000327554,0.00002426383,0.0005968735],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8395142,"threshold_uncertainty_score":0.999997,"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."}}