Bibliographic record
Abstract
The vitality and sustainability of rural communities in Canada requires supports, policies, practices and people, dedicated and creative people. Rural research centres seek to aid in supporting rural vitality by offering needed information through researching on the benefits of investments, innovation, and durability of rural life, as well as sustaining important partnerships with diverse networks of stakeholders in rural regions. This panel will share recent survey results from the RPLC network that is inviting collaboration among Canadian rural research centres, the Rural Research Centres Network (R2CN). Among other findings, the R2CN is inviting more engaged research through community partner collaborations and working creatively to better leverage the resources and insights of rural research centres (RRC). The report writing and data collection and policy advocacy work that emerges from RRC has had great impact in Canada and elsewhere. Might we be in a time for reinvigoration and repositioning of these centres for a more central role in the discourse on the vibrancy of rural life? Come share your ideas on the importance of facts driving policy along with the power of story telling and community building for rural life in Canada.
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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.045 | 0.018 |
| Scholarly communication | 0.024 | 0.008 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 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".