{"id":"W4210784790","doi":"10.1177/15248399211072532","title":"Educating for Children’s Health: Lessons Learned on Facilitating Media Literacy & Food Marketing Programming","year":2022,"lang":"en","type":"article","venue":"Health Promotion Practice","topic":"Digital Communication and Language","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Media literacy; Food marketing; Marketing; Health literacy; Literacy; Health promotion; Promotion (chess); Public relations; Medical education; Social marketing; Business; Psychology; Medicine; Advertising; Public health; Political science; Pedagogy; Nursing; Health care","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts"],"consensus_categories":[],"category_scores_codex":[0.009665584,0.0001845257,0.0002415019,0.0001966593,0.002141271,0.0004580273,0.001046738,0.00003110981,0.00003664511],"category_scores_gemma":[0.0107501,0.0002067364,0.00008413699,0.0006540769,0.00002177804,0.001407542,0.0006477443,0.0007066818,0.00002462624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000452206,"about_ca_system_score_gemma":0.0007640343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001139902,"about_ca_topic_score_gemma":0.00004176659,"domain_scores_codex":[0.9945151,0.002953794,0.0007123273,0.0006126107,0.0005654566,0.0006407482],"domain_scores_gemma":[0.9930225,0.00429058,0.001104269,0.001135093,0.0001896971,0.0002578393],"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":[0.00003430823,0.000914994,0.00003021048,0.0001608421,0.00001299381,4.158999e-7,0.02205978,0.00005661663,0.000003646652,0.02926684,0.001318483,0.9461409],"study_design_scores_gemma":[0.001571272,0.00371448,0.002330623,0.0003895363,0.00001008851,0.0002888682,0.01428186,0.003544999,0.00001180234,0.002264234,0.9709203,0.0006719363],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01436615,0.002313959,0.1720356,0.7978272,0.0009433409,0.004673669,0.0001567282,0.001166009,0.006517333],"genre_scores_gemma":[0.5753405,0.0000586381,0.4112056,0.0115062,0.00008796027,0.001057114,0.0003449212,0.0000381278,0.00036097],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.9696018,"threshold_uncertainty_score":0.9991578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1401089892743819,"score_gpt":0.4388768179168825,"score_spread":0.2987678286425006,"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."}}