Bibliographic record
Abstract
Rural places face unique challenges when it comes to climate change. Rural communities have, and will continue to see the impacts of climate change, necessitating action in the form of both ongoing mitigation (i.e., action to slow climate change) and adaptation (i.e., action to address the impacts of climate change. These communities often face difficulties in obtaining locally specific data and information, and are also subject to capacity limitations and limits in jurisdiction and mandate. However, rural places are also home to innovative and place-based ideas for tackling the emerging and complex realities of climate change.This panel brings together participants from the Columbia Basin Rural Development’s pilot project to test and refine indicators of climate change adaptation at the local scale. This project includes post-secondary researchers, subject matter experts, students and community staff. Project participants are working together to develop a process and supporting resources that will allow rural communities to better understand their local context with respect to climate change, tracking relevant changes, and identifying areas for local action. Panel participants will discuss:• The importance of local action on climate change• Their experiences and lessons learned from the project• Next steps
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 | 0.035 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.019 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".