Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
How do we learn from each other for the betterment of our communities? Facilitated by the BC Rural Network (BCRN) as a forum to share the challenges and successes of rural networks within BC and across the country, this session focuses on the role grass-roots networking organizations play in bettering the lives of rural Canadians. CRRF conference participants are invited to take part in a discussion of key questions like: How are the tools, resources and experiences which are important to rural communities identified, coordinated and disseminated? How are linkages between communities, rural organizations, researchers and policy-makers built? How do rural networks operate effectively and evolve to meet changing community needs? This interactive session will be an opportunity for us to learn from each other about how to build and maintain rural networks and communities. The BCRN was established as a community driven non-profit in 2004 with the goal of building stronger rural and remote communities in BC by promoting better understanding of rural issues across all jurisdictions and developing responses to rural issues. Please see www.bcruralnetwork.ca for more information.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it