Environmental Disaster Management in the Great Lakes: To What Extent are Governments in the Region Prepared?
Bibliographic record
Abstract
Introduction -- Natural and man-made disasters are threatening countries and causing fatal damages to the economy, the environment and the livelihood and well-being of people across the globe (Zhang and Huang, 2018). Scholars in many disciplines and practitioners in several fields have realized the significance of natural and environmental disasters for decades. The study of environmental disasters is interdisciplinary and requires a broad range of knowledge and research to understand the natural and human dimensions of disasters. Some environmental disasters are known to be directly caused by human actions and behavior, while others are beyond human control and understanding. Reducing associated risks with existing and potential hazards that threaten people and societies is the ultimate goal of disaster management (O'Brien et al., 2006) at local, regional and international scales. Disaster management is based on the belief that human intervention and action can help humans mitigate and adapt to disasters. There is an extensive scholarly and practitioner literature related to disaster management, as virtually all jurisdictions have to grapple with environmental disasters. Yet, capacity for human intervention, ‘management’ and action remain limited in the context of uncertainty related to many types of natural disasters. In addition, there are a wide range of government, non-government and private sector actors involved in environmental disaster management. This thesis focuses on government-led regimes related to environmental disaster management in the Great Lakes region. The introduction covers the significance of disasters globally, in Canada and the United States, and in the Great Lakes region. It introduces key concepts and dimensions of environmental disasters; outlines the objectives of this research project; the central research questions; and provides an overview of the structure of the thesis. Prior to a focus on environmental disasters in the Great Lakes, the researcher had an interest of exploring environmental disaster management in the Red Sea region in Saudi Arabia. The goal was to investigate transboundary and national government arrangements related to environmental disasters in the Red Sea within the borders of the Kingdom of Saudi Arabia, Egypt and Sudan. A comparative research approach was considered to study both regions and compare government arrangements. However, due to the lack of publically available government documents and some government restrictions on data related to environmental disasters management in the Red Sea, a focus on environmental disaster management in that region and a comparative analysis was not feasible to conduct. Preliminary research indicated that there was not a lot of research on this topic in the Great Lakes region. Therefore, this thesis focuses on environmental disaster management in the Great Lakes region of North America providing some research foundations for possible comparative research in the future.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".