{"id":"W2604575007","doi":"10.1145/3017680.3022434","title":"What We Say vs. What They Do","year":2017,"lang":"en","type":"article","venue":"","topic":"Teaching and Learning Programming","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Outreach; Mainstream; Variety (cybernetics); Diversification (marketing strategy); Computer science; Coding (social sciences); Public relations; World Wide Web; Sociology; Social science; Political science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005730213,0.0001028329,0.000106602,0.00003933113,0.0007243813,0.01319147,0.001695955,0.00005005226,0.00002666446],"category_scores_gemma":[0.00007309406,0.00007764258,0.00006173208,0.00002616453,0.00003641085,0.00529594,0.0004436488,0.0002296006,0.0003171944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001402036,"about_ca_system_score_gemma":0.00002114148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001287273,"about_ca_topic_score_gemma":0.00003887831,"domain_scores_codex":[0.9990447,0.00006940027,0.00009843763,0.0003210889,0.0001964293,0.0002699762],"domain_scores_gemma":[0.998456,0.0000640541,0.00009207214,0.001277737,0.00002443268,0.00008572853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001041334,0.00001468025,0.0005018471,0.000003437683,0.000005365308,0.000007074383,0.003665986,0.00001328518,0.000008606201,0.01911135,0.0006375217,0.9760298],"study_design_scores_gemma":[0.0002559552,0.00008449001,0.004102556,0.0002538213,0.000004117838,0.00002067251,0.0011214,0.00314553,0.0001587509,0.003052707,0.987517,0.0002830207],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0466457,0.006962199,0.6843795,0.2191603,0.01803022,0.0004502521,1.873753e-7,0.003006337,0.02136525],"genre_scores_gemma":[0.9322183,0.0006965235,0.05413717,0.0006361274,0.0001573545,0.000005652777,3.368123e-7,0.00001035809,0.01213814],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9868795,"threshold_uncertainty_score":0.9878329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02721729491054234,"score_gpt":0.2914630055201056,"score_spread":0.2642457106095633,"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."}}