Pragmatism, projections, priorities, plans and politics in Prince George: Adapting to climate change in a Canadian community.
Bibliographic record
Abstract
The two principal human responses to climate change are adaptation and mitigation. A small, but growing, number of scientific and professional efforts are focusing toward adaptation, as it becomes clear that mitigation efforts alone can no longer effectively minimize the negative impacts of climate change. Local governments are well suited to undertake proactive adaptation measures due to their abilities to apply social capital, act quickly, and implement actions that can provide direct benefits for residents. Like many northern communities, the City of Prince George, Canada, has been experiencing rapid rates of climate change. City practitioners have been responding to changing conditions in the region for years, and expressed interest in formally exploring adaptation with researchers. During a focused one-day workshop, an overview of climate change and past climate trend and future projection information were presented to local staff and stakeholders, and applied to determine impact priorities. The workshop outcomes were triangulated with community feedback to create an adaptation strategy for Prince George. Changes to forests and increased flooding are the top local priorities, and impacts related to transportation infrastructure, severe weather and water supply are high priorities. Other impacts, such as agricultural changes, are important but did not rank highly using a risk assessment framework. The adaptation strategy precipitated further local engagement and action. Researchers participated in the processes to create a sustainability plan and update the Official Community Plan for Prince George. Many adaptation measures were integrated into both documents. Factors enabling the incorporation of adaptation included the high level of local knowledge and existing adaptation strategy. Many barriers, including limited policy direction and a lack of priority, continue to pose challenges in mainstreaming adaptation into local plans. Additional research focused on forests, flooding, transportation infrastructure
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.048 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 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".