Creating coherence in global environmental governance: Canada's 2002 opportunity
Bibliographic record
Abstract
The international community has been generating multilateral environmental agreements at the bilateral, regional, plurilateral, and global level for well over a century, with a notable increase since the UNCED Rio conventions of 1992.Yet there is considerable doubt about the comprehensiveness, coherence and effectiveness of the cumulative assemblage, especially as Canada and its global partners confront such critical environmental challenges of the twenty-first century as fulfilling their climate change commitments, and forging new conventions on forests and freshwater.How can Canada best lead in making the galaxy of multilateral environmental agreements and their implementing international institutions more effective?Canada has long been a successful pioneer in generating multilateral environmental agreements and institutions for the global community, and its legacy in doing so generates exceptional domestic unity and international respect.Yet Canada and its global partners face a new generation of challenges in making the growing galaxy of multilateral environmental agreements and institutions work in a twenty-first century world.Intensifying ecological interdependence calls for new environmental regimes, and ones that operate in a more co-equal, co-ordinated and coherent fashion with those dealing with specific media or issues and in interrelated areas such as trade, finance, and investment.The prospective "Rio plus ten" review and Canada's hosting of the G8 Summit in the year 2002 provide an important opportunity for Canada to lead in the design and delivery of a more coherent and effective system of global environmental governance.
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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 | 0.012 | 0.008 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".