The geography of climate change in a rural resource-dependent town: the case of McBride, British Columbia
Bibliographic record
Abstract
British Columbia's (BC) climate change policy has put pressure on the province's rural communities to become 'carbon-neutral'.This research examines the experience of one such community, McBride, a resource-based town in north-eastern BC struggling to reduce its emissions while also trying to restructure its depressed economy.The thesis asks what opportunities and challenges McBride faces to reduce its emissions and transition to a 'low-carbon' community.It focuses on the politics of scale, asking what role the 'local' plays in climate change mitigation, and the distinctive qualities of the 'rural' local scale in these efforts.McBride has several opportunities that might abet the community's transition to a "green economy": abundant renewable energy resources, potential value-added commodity production in the forest sector, and an emerging local agricultural movement.But it also faces geographically specific obstacles to reaching these goals: limited financial and human capital, internal division, restricted political capacity, isolation, and inadequate and misplaced articulations with higher levels of governance.The thesis argues that to confront the impact of climate change, BC must move beyond its urban political bias, and work with the geographic specificities of rural communities across the province.This requires taking a regional approach: building up horizontal connections between rural communities, decentralizing industry and populations across BC, and increasing political and economic resource distribution outside of major population centres.Higher scales of governance must reconceive rural communities, not as technical problems to be 'fixed' with 'aid', but as collaborators in democratic, political, economic, and ecological change.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".