{"id":"W4415250187","doi":"10.1145/3757673","title":"The Human Labour of Data Work: Capturing Cultural Diversity through <scp>World Wide Dishes</scp>","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ACM on Human-Computer Interaction","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Citizen journalism; Variety (cybernetics); Participatory GIS; Process (computing); Participatory action research; Citizen science; Diversity (politics); Participatory design","routes":{"ca_aff":true,"ca_fund":false,"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":["sts","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0006192117,0.0003221257,0.0003510028,0.000345933,0.001494491,0.000381448,0.01001641,0.0001243387,0.000005062499],"category_scores_gemma":[0.0005417667,0.0002216275,0.0001638075,0.001239587,0.000330622,0.003356386,0.01343737,0.0009438512,0.00001071155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002667316,"about_ca_system_score_gemma":0.00002109379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000163022,"about_ca_topic_score_gemma":0.0000580471,"domain_scores_codex":[0.9976133,0.00004411737,0.0006984905,0.0007507856,0.000499728,0.0003936144],"domain_scores_gemma":[0.9956756,0.0005521337,0.001103806,0.001805196,0.0008379256,0.0000253801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000678428,0.0005688033,0.04165255,0.0002900847,0.0007489101,0.000002904249,0.008897272,0.00006803711,0.03871579,0.6941856,0.2007174,0.01408483],"study_design_scores_gemma":[0.001813036,0.0004595464,0.2030274,0.002928768,0.0001987871,0.00003560532,0.003034562,0.004181794,0.5601755,0.1680878,0.05548182,0.0005753851],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.983068,0.00002734999,0.003888671,0.003440929,0.002215149,0.0004370291,0.000006167176,0.0002364648,0.00668028],"genre_scores_gemma":[0.9922695,0.000007198896,0.004489928,0.0004975341,0.0001832353,0.00001562979,0.00000673495,0.00001473523,0.002515554],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5260978,"threshold_uncertainty_score":0.9998055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09436516645429642,"score_gpt":0.351507180276031,"score_spread":0.2571420138217346,"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."}}