Barriers to and Opportunities for Intergovernmental Conflict Resolution: A Case Study of the Trans Mountain Pipeline Expansion
Bibliographic record
Abstract
Federalism can exacerbate tensions around the uneven geographical distribution of natural resources. Related conflicts recur in Canada, a federal state with an uneven distribution of petroleum products across its provinces and territories. A salient example of intergovernmental conflict over petroleum products is the dispute over the Trans Mountain pipeline expansion project. This research examines the conflict among the Governments of Canada, British Columbia, and Alberta around Trans Mountain, focusing on the barriers to intergovernmental conflict resolution and mitigation in Canada and the requirements any policy options must fulfill to overcome these barriers. A mainly qualitative approach addresses these issues. Specifically, this research combines global energy governance and John L. Campbell’s typology of ideas to create a new approach. Campbell is more central to this research. This approach is applied to a secondary statistical analysis of public opinion polling, a thematic analysis of key actors’ public documents, and an analysis of interviews I conducted with key actors. \nThis research finds that together, competitive federalism and the joint decision trap prevent conflict resolution. Accordingly, this research produces a list of barriers to resolving this intergovernmental conflict and requirements for mitigating this conflict. By identifying these requirements, I create and apply an original approach that future studies can use to test the likelihood of success for policy options to mitigate similar intergovernmental conflicts over natural resources. This research’s evaluation of potential mitigation tools suggests that 1) federal and provincial teams dedicated to large projects help bureaucrats complete these projects; 2) policies protecting the environment decrease tensions among actors; and 3) leveraging communication through partisan affiliations decreases tension.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.031 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".