Bibliographic record
Abstract
Building on our empirical studies in rural well-being this session will discuss complex nature of the challenges and the opportunities in rural contexts. The complexity of the problems necessitates multi-dimensional solutions. Strategies are sought that effectively offer agriculture-supporting, health-enhancing, damage-preventing, environmentally-sustaining and community-rejuvenating results. Rural communities face multiple external and internal stresses (Aked, Marks, Cordon, and Thompson, 2011; Cox, Frere, West, and Wiseman, 2010). The challenges our rural communities face are simultaneously technical, cultural, political, social, ethical, economic, and environmental. The efforts needed to work through the complexity and wicked issues are worthwhile as collaborators can harvest greater community resiliency, prosperity, sustainability, and vibrancy. This workshop will offer policy makers, practitioners, educators, and professionals along with industry leaders a helpful distillation of the literature on well-being and how our adaptations of the Canadian Index of Well-being can be a useful tool to ignite imagination for measuring and enhancing rural resiliency, prosperity, sustainability, and vibrancy. In this interactive workshop participants will be invited to share their experiences with what opportunities they see in these times of unprecedented climate change, pandemics of non- communicable (lifestyle) diseases, and declining economic vitality (Davis, Crothers, Grant, Young and Smith, 2012; Farmer, Prior and Taylor, 2012). Forging ways to work together across disciplines to enhance rural well-being will be encouraged. Examples will be shared of emerging strategies and successes like small scale production, cooperatives, and proactive farming communities contributing to the bio-economy and circularity among many other innovative opportunities ahead.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".