{"id":"W3095298179","doi":"10.1016/j.marpol.2020.104280","title":"The quality of fisheries governance assessed using a participatory, multi-criteria framework: A case study from Murcia, Spain","year":2020,"lang":"en","type":"article","venue":"Marine Policy","topic":"Cognitive Science and Mapping","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"H2020 Marie Skłodowska-Curie Actions; Horizon 2020; Horizon 2020 Framework Programme","keywords":"Corporate governance; Stakeholder; Sustainability; Business; Environmental resource management; Quality (philosophy); Citizen journalism; Stakeholder engagement; Normative; Environmental planning; Good governance; Fishery; Political science; Geography; Economics; Ecology; Public relations","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":[0.0007309839,0.0001513131,0.0002573722,0.00002658052,0.0002871935,0.0002681438,0.0007336715,0.00003575577,0.00002398529],"category_scores_gemma":[0.002193692,0.0001203542,0.00007166709,0.0007402424,0.0001470011,0.000394003,0.001269235,0.0001647963,0.000005730688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004646942,"about_ca_system_score_gemma":0.0002149666,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05682375,"about_ca_topic_score_gemma":0.003024276,"domain_scores_codex":[0.998132,0.0003807488,0.0004032061,0.0004079042,0.0003217622,0.0003544107],"domain_scores_gemma":[0.998418,0.0005476458,0.0002452226,0.0005300369,0.0001196508,0.0001394505],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001057344,0.001226893,0.5899163,0.00009539149,0.0001930661,0.001315031,0.08312404,0.00005869574,0.00821653,0.02257744,0.0002157341,0.2929551],"study_design_scores_gemma":[0.00156601,0.000324209,0.7172779,0.00006031754,0.00003555192,0.00003785581,0.009121131,0.2626651,0.001215067,0.004983515,0.002087727,0.0006255794],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8651503,0.00002299419,0.1303709,0.003842696,0.0001196619,0.0002490082,0.00001595382,0.00005985379,0.0001687],"genre_scores_gemma":[0.9843989,0.000004512121,0.01417074,0.001185872,0.0001891212,0.00001954124,5.020775e-7,0.000007390446,0.00002342282],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2923295,"threshold_uncertainty_score":0.9494569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2339999238905059,"score_gpt":0.4246712632503138,"score_spread":0.1906713393598079,"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."}}