Drawers of Oil, Farmers of Wind? Common Sense, National Identity and Rural Landscapes in Canadian Climate Politics
Bibliographic record
Abstract
Despite the growing global consensus on the need for action to combat climate change, transitions to more sustainable practices will not be simple. This is especially true in the case of Canada which is a) country that has increasingly relied on its fossil fuel sector as a primary driver of economic growth, and b) a federal state where the division of power over the energy and resource sectors are shared between the federal and provincial governments. Further complicating this is Canada’s long history of natural resource extraction and its connection to Canadian national identity. After a decade of assertive support for the oil sector by the federal Conservative Party, the Liberal Party won a majority government partly due to promises to take meaningful climate action. However, despite these promises the Liberals have continued to wholeheartedly back the industry centred in the Alberta oil sands, and attempts to balance climate action with support for the oil industry has seen the balance swing heavily in favour of the latter. The ability of the oil industry and its allies in civil society to equate it with Canadian national identity and ‘common sense’ has entrenched its hegemony in the Canadian economy. In Ontario, the policies of a clean energy transition primarily via wind energy initiated by former Premier Dalton McGuinty have been successfully challenged by opposition at both the municipal and provincial level. One of the major obstacles has been that wind energy projects clash with, rather than fit into, ideas of rurality and what Canada is. These case studies highlight the challenges involved in green transitions, particularly in locations where natural resource extraction has historically been a central component of national identity and the national economy.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.050 | 0.034 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".