{"id":"W4407379580","doi":"10.1080/09537287.2025.2456959","title":"Paradoxes and trade-offs in the front-end process of large public projects","year":2025,"lang":"en","type":"article","venue":"Production Planning & Control","topic":"Construction Project Management and Performance","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Arts and Humanities Research Council","keywords":"Front and back ends; Process (computing); Process management; Business; Front (military); Computer science; Operations management; Industrial organization; Engineering; Economics; Mechanical engineering; Operating system","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":[0.02462887,0.0006983931,0.0006842499,0.002449508,0.005983511,0.01712698,0.002080392,0.003869419,0.004108359],"category_scores_gemma":[0.03992038,0.0006365876,0.0009144502,0.002184981,0.01890798,0.01408824,0.008205253,0.004046105,0.0003881797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00624734,"about_ca_system_score_gemma":0.004285349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001946138,"about_ca_topic_score_gemma":0.001827509,"domain_scores_codex":[0.971559,0.02135303,0.0005805104,0.0009863449,0.003897323,0.001623752],"domain_scores_gemma":[0.963616,0.02887533,0.002505034,0.001778467,0.001682796,0.001542454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001425611,0.0001792054,0.002018223,0.0002069196,0.00002981263,0.001333912,0.03251419,0.008926243,0.001107874,0.9325985,0.0007105272,0.02023199],"study_design_scores_gemma":[0.000062757,0.0001612942,0.002679545,0.0002905681,0.0000211354,0.0005259031,0.04603928,0.02352724,0.001180516,0.9080819,0.01731829,0.0001115716],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5371757,0.002213723,0.2845702,0.01162115,0.0001553015,0.0005643281,0.00005934984,0.000161639,0.1634786],"genre_scores_gemma":[0.9763417,0.0003189793,0.02037198,0.0001220688,0.00001724118,0.0001237986,0.00001397543,0.00001944323,0.00267073],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02462887,"threshold_uncertainty_score":0.1302515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05698222758623264,"score_gpt":0.361598030861032,"score_spread":0.3046158032747993,"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."}}