Burning money to save the environment : an interest-based analysis of Canada’s implementation plan for the Kyoto protocol
Bibliographic record
Abstract
On December 17 2002, Canada ratified the Kyoto Protocol to the United Nations Framework Convention on Climate Change. This binding international agreement committed Canada to reducing its greenhouse gas (GHG) emissions by 6% below 1990 levels between 2008-2012. Following Canada's ratification, a struggle ensued between various interest groups over how best to achieve the reductions mandated by the Protocol. The federal government engaged in a broad consultative process with all concerned interests which ended in the release of "Moving Forward on Climate Change - A Plan for Honouring our Kyoto Commitment" in April of 2005. This 48-page policy document describes in detail Canada's implementation strategy and forms the basis of analysis for this thesis. The plan outlined in "Moving Forward" is heavily reliant on government expenditure while relatively sparse on direct regulatory measures. This thesis will attempt to explain why the government chose to focus on indirect measures like expenditure, to the exclusion of more direct initiatives such as regulation. It will do so by conducting an interest-based analysis of the forces which helped structure the government's implementation plan. It will theorize the consultative process as a "bargaining game" wherein the four main actors (the federal government, the business community, the Government of Alberta, and the ENGO community) each acted upon a list of prioritized interests to achieve an optimal policy outcome. It will conclude that the business community and the Government of Alberta were successful in their bid to move the government away from a more coercive regulatory plan. They succeeded largely because their efforts to create dissent over the economic ramifications of a coercive regulatory scheme presented a credible threat to the government's future electoral prospects. In addition to this, ENGOs and environmentally-minded government officials did not possess the broad public support necessary to push for a more aggressive approach. The result is an implementation plan that distributes the costs of GHG reductions across the broader Canadian public instead of concentrating them on the most polluting industries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".