The management of diversity : flexible integration in enviromental law through enhanced cooperation in the EU and harmonization in Canada
Bibliographic record
Abstract
This thesis compares two mechanisms of flexible integration in environmental policy in two different legal systems, Canada and the European Union. It looks at the surrounding circumstances that led to the creation of these mechanisms, examines their design and functioning, attempts to assess their achievements and potential and situates them in a broader international context of current trends in federal systems. My point is that federal systems have had to turn to some form of flexible integration in order to prevent lowest common denominator solutions. They have chosen different solutions, but the examples show that effective environmental policy through flexible integration could be possible. Chapter One sets out the methods that are used in this comparative study. Comparative law is a logical choice to deal with the topic. I argue that the EU and Canada have a baseline of similarity and can be compared as two federal systems. Furthermore, I use federalism as a lens through which to look at enhanced cooperation in the EU and harmonization in Canada. Chapter Two explains the Canada-wide Accord on Environmental Harmonization and investigates its achievements to date under the Canada-wide Sub-Agreement on Environmental Standards. I question the design of the mechanism and its solely political character. Some results have been achieved under the Standards Sub-Agreement and considering the background of the Canada-wide Accord, one should not be too dismissive about these achievements. I argue, however, that the mechanism's potential could be considerably increased. Chapter Three describes the EU provisions on enhanced cooperation and their potential application in environmental law. I show that although the legal prerequisites are numerous and seem very strict, the mechanism has great potential if the conditions for application are not interpreted narrowly. My example is the new chemicals policy (REACH), but I emphasize that many other areas in environmental policy could be addressed effectively through enhanced cooperation. Chapter Four compares the two mechanisms and explores their differences and similarities. I explain the major problem areas and try to offer some suggestions for improvement based on the comparison. The Conclusion sums up and evaluates the findings of each chapter.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".