{"id":"W2114173553","doi":"10.18352/ijc.62","title":"Developing Multi-Level Institutions from Top-Down Ancestors","year":2007,"lang":"en","type":"article","venue":"International Journal of the Commons","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Corporate governance; Top-down and bottom-up design; Matching (statistics); Resource (disambiguation); Resource management (computing); Scale (ratio); Business; Common-pool resource; Environmental resource management; Computer science; Ecology; Economics; Geography; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.0003285606,0.00005620564,0.00006547529,0.00003820708,0.0001404331,0.00001818407,0.0006790989,0.00004185192,0.00025495],"category_scores_gemma":[0.0001843623,0.00004061942,0.00006870618,0.00009784011,0.0001053256,0.0002353376,0.0001731353,0.0001825336,0.00006434623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003174079,"about_ca_system_score_gemma":0.00006298329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002169378,"about_ca_topic_score_gemma":0.003019543,"domain_scores_codex":[0.9992836,0.00003440495,0.000267527,0.00006317574,0.0002588355,0.00009250207],"domain_scores_gemma":[0.9994628,0.0001281265,0.0002311182,0.00009226507,0.00004764025,0.00003805109],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002265008,0.00005352712,0.9899357,1.573846e-7,0.00004405113,0.00001539232,0.0001853939,0.0006767691,0.0005910519,0.001541668,0.002218329,0.0047153],"study_design_scores_gemma":[0.0002792079,0.000007468209,0.9706639,0.00002251151,0.000008905089,0.0000462261,0.00004867627,0.00007057742,0.0008594785,0.001655422,0.02628679,0.00005086056],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.943403,0.00002077873,0.04244778,0.01033003,0.002212485,0.00003513258,0.000008046931,0.00000552329,0.00153723],"genre_scores_gemma":[0.9885848,0.000006895802,0.009444421,0.001617723,0.0001543519,7.080368e-7,0.000001666153,0.000002932895,0.0001864634],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04518184,"threshold_uncertainty_score":0.2791524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09755460247446718,"score_gpt":0.3251988378543836,"score_spread":0.2276442353799165,"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."}}