{"id":"W4408605475","doi":"10.1016/j.multra.2025.100223","title":"Evaluating the usefulness of VGI for citizen co-producing city services from citizen perspective: A case study of crowdsourcing pedestrian navigation","year":2025,"lang":"en","type":"article","venue":"Multimodal Transportation","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Crowdsourcing; Volunteered geographic information; Perspective (graphical); Pedestrian; Citizen science; Computer science; Data science; Transport engineering; Engineering; World Wide Web; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006466733,0.0004658097,0.0003974635,0.00140918,0.004450957,0.00250156,0.0009662474,0.00178258,0.001548745],"category_scores_gemma":[0.01312764,0.0002763945,0.0004939335,0.001353062,0.002326621,0.002014233,0.002543875,0.001020369,0.000396173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002133031,"about_ca_system_score_gemma":0.002081705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01444531,"about_ca_topic_score_gemma":0.0374501,"domain_scores_codex":[0.9945479,0.004047671,0.0001506433,0.0003082716,0.0004960485,0.0004493322],"domain_scores_gemma":[0.9862148,0.009503914,0.0009724941,0.0007888163,0.00177416,0.0007457648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.000442163,0.003354172,0.1280462,0.0008163962,0.00007817482,0.009261281,0.7694796,0.00147491,0.008550424,0.002790418,0.003233101,0.07247315],"study_design_scores_gemma":[0.00008193697,0.001903536,0.0553692,0.0002857788,0.00009988456,0.001273356,0.9042928,0.006040847,0.005393839,0.001146754,0.02397853,0.0001335059],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951053,0.00003300009,0.001136032,0.0002294974,0.000009291401,0.0001669752,0.00004743179,0.0000168447,0.00325581],"genre_scores_gemma":[0.9946185,0.00009922459,0.003667993,0.0001114077,0.000007712551,0.0001871277,0.0000483726,0.00001664245,0.0012431],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01444531,"threshold_uncertainty_score":0.03419977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06637986463921254,"score_gpt":0.3691605947367774,"score_spread":0.3027807300975648,"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."}}