Governança e gestão dos recursos hídricos no contexto das mudanças climáticas: um estudo com empresas do setor de alimentos no Canadá e seus stakeholders
Bibliographic record
Abstract
The research builds a framework for assessing climate change risk perception on water resource governance and water management in Canada's food businesses and stakeholders. Climate change and its consequent changes in water resources directly affect industries and, more specifically, food companies. The use of water in this industry is high and the availability of clean water is a necessity. The Canadian food industry is the second largest in the country in terms of production value and is directly affected by these climate and water issues. However, there is not always a perception of risks associated with climate change and changes in water resources. This perception directly affects the institutional environment and mechanisms of governance of water resources and consequently the management of water resources in companies and in the city. Thus, by analyzing food companies in Canada and its stakeholders, an exploratory, descriptive and qualitative research is developed. The research was conducted through semi-structured interviews with sixteen actors from the Canadian context, who bring an understanding of the issues under study. These actors are the companies and their stakeholders: government, academia, agencies, business associations and NGOs. The data were analyzed through a content analysis. The results of the research show the poor influence of the perception of climate change risks in the Governance and Management of Water Resources. The mechanisms of water governance are not structured by concern with climate change and changes in water resources. Institutional capacities need to be further developed and established. Management of water resources in enterprises is more concerned with wastewater and companies are more concerned with costs and with compliance. The results contribute to the understanding of the key issues that permeate governance and water management in a context of abundance of resources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".