{"id":"W2189472627","doi":"10.1080/07011784.2015.1088403","title":"Towards sustainable water governance: Examining water governance issues in Québec through the lens of multi-loop social learning","year":2015,"lang":"en","type":"article","venue":"Canadian Water Resources Journal / Revue canadienne des ressources hydriques","topic":"Evaluation and Performance Assessment","field":"Decision Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; McGill University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Corporate governance; Watershed; Watershed management; Business; Credibility; Knowledge management; Social learning; Public relations; Environmental resource management; Process management; Political science; Computer science; Economics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.008324246,0.0004804094,0.0008316949,0.000429619,0.001552971,0.0009626989,0.001864389,0.0002449074,0.0008783275],"category_scores_gemma":[0.001207519,0.000248118,0.0002280067,0.0005031929,0.000743038,0.001604281,0.0002719921,0.001071007,0.00009038597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002522066,"about_ca_system_score_gemma":0.00018301,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7217107,"about_ca_topic_score_gemma":0.9067256,"domain_scores_codex":[0.993129,0.001196314,0.001556827,0.0006604219,0.001139236,0.002318202],"domain_scores_gemma":[0.9965577,0.0001777148,0.0005125005,0.0005769767,0.00141637,0.0007588115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008581702,0.00003062543,0.01690307,0.00005625503,0.00006857919,0.0003970611,0.9619595,0.0120074,0.000721006,0.00005242041,0.000361244,0.007357042],"study_design_scores_gemma":[0.0008468144,0.0002659884,0.006626321,0.0001535567,0.00002965869,0.0001922456,0.0426922,0.00179281,0.006244902,0.001577982,0.9391165,0.0004610478],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9783839,0.002138667,0.00003597746,0.01397931,0.0002885484,0.0003137617,0.00002132027,0.00002680329,0.004811727],"genre_scores_gemma":[0.9736757,0.0004250494,0.000155689,0.0005745444,0.0005305869,0.00003172188,0.0000149877,0.00006518819,0.02452655],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9387552,"threshold_uncertainty_score":0.9999971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1313460584901011,"score_gpt":0.355741528265282,"score_spread":0.224395469775181,"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."}}