{"id":"W1992076436","doi":"10.1177/0042098010375995","title":"Planning Context and Urban Intensification Outcomes","year":2010,"lang":"en","type":"article","venue":"Urban Studies","topic":"Urban Planning and Governance","field":"Social Sciences","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"NIMBY; Framing (construction); Incentive; Politics; Metropolitan area; Context (archaeology); Political science; Public policy; Public opinion; Government (linguistics); Political economy; Public administration; Sociology; Economic growth; Economics; Geography; Engineering; Market economy; Civil engineering; Law","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":[],"consensus_categories":[],"category_scores_codex":[0.0003312229,0.00009787604,0.0001867743,0.00003444766,0.0005438424,0.00005147395,0.0001068465,0.00005761419,0.0000096801],"category_scores_gemma":[0.001274546,0.00008190476,0.00002920554,0.0000867355,0.0004714013,0.0001264666,0.00004127072,0.0001743491,0.0000210811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002192261,"about_ca_system_score_gemma":0.00002575859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004044647,"about_ca_topic_score_gemma":0.001057334,"domain_scores_codex":[0.9992504,0.00003648891,0.0001308372,0.0001900097,0.0001842365,0.0002080907],"domain_scores_gemma":[0.9992935,0.000310491,0.00009190327,0.0001234422,0.0001192201,0.00006146698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000004598056,0.000008804071,0.7439369,0.000004357776,0.00004412532,0.000002973138,0.05848206,7.544845e-8,0.00007616937,0.02099029,0.1751415,0.00130815],"study_design_scores_gemma":[0.0001933786,0.00002076576,0.3582465,0.00003394899,0.00002276018,9.972495e-7,0.03581828,0.000006794047,0.00004278712,0.00060287,0.604843,0.0001678685],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9711979,0.008480784,0.00001312065,0.00451448,0.001133516,0.0001317082,0.00001261424,0.0001481799,0.01436771],"genre_scores_gemma":[0.9876534,0.00009775332,0.0001276752,0.000478205,0.0002648364,0.00001072387,0.000001678077,0.000006830771,0.01135895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4297016,"threshold_uncertainty_score":0.4182852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06090645714662955,"score_gpt":0.3528078657648913,"score_spread":0.2919014086182617,"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."}}