{"id":"W3208162475","doi":"10.1016/j.habitatint.2021.102458","title":"High-quality development in China: Measurement system, spatial pattern, and improvement paths","year":2021,"lang":"en","type":"article","venue":"Habitat International","topic":"Regional Economics and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":169,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Lagging; China; Context (archaeology); Geography; Scale (ratio); Index (typography); Regional science; Economic geography; Economic growth; Economics; Cartography; Statistics; Computer science; Mathematics","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":[0.002558226,0.0003480381,0.0004016544,0.004376449,0.0006589884,0.001273613,0.0008342774,0.0002466175,0.001192149],"category_scores_gemma":[0.004842842,0.0002483392,0.000397138,0.009636815,0.000761734,0.001055488,0.001186867,0.0003098705,0.0001242241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004842831,"about_ca_system_score_gemma":0.007243245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.245183,"about_ca_topic_score_gemma":0.3028689,"domain_scores_codex":[0.9982836,0.0003831228,0.0002058435,0.0003512571,0.0004467034,0.0003293768],"domain_scores_gemma":[0.9921943,0.0009614057,0.001788398,0.0005304695,0.003683656,0.0008418217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002989282,0.00002599078,0.988577,0.00003525303,0.00007152828,0.00003003939,0.0002430073,0.001005817,0.0001079077,0.001081656,0.0006174971,0.008174445],"study_design_scores_gemma":[0.000004742335,0.00002084756,0.9963056,0.00001029722,0.00003111676,0.00001197727,0.000281328,0.002416628,0.00009239581,0.0002631753,0.0005556834,0.000006185755],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939027,0.0004281337,0.0008653678,0.0002936168,0.000009361328,0.00003056194,0.002061722,0.00003347941,0.002375134],"genre_scores_gemma":[0.9983583,0.00007535837,0.0003799644,0.00001140319,0.000002924526,0.0000116843,0.0007465372,0.000002557691,0.0004111819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.245183,"threshold_uncertainty_score":0.4875118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03019752760893139,"score_gpt":0.2174751581882464,"score_spread":0.187277630579315,"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."}}