Common Ground Canada Network: Building relationships for just and sustainable agriculture and food systems transitions
Bibliographic record
Abstract
Introduction The Common Ground Canada Network[1] (CGCN) is a national partnership of social science and humanities (SSH) researchers, community organizations, Indigenous leaders, farmers, policymakers, and civil society groups working together to transform Canada’s agriculture and food systems in pursuit of a sustainable, net-zero future. CGCN recognizes that climate change is not just a technical problem requiring the expertise of natural scientists and engineering; it is also a problem of relationships between people and the land, between rural and urban communities, between Canada and the world, and between people within/and food systems. An initial team of 49 academics at 13 institutions and 22 nonprofit/nongovernmental organizations responded to a joint opportunity from the Social Sciences and Humanities Research Council (SSHRC) and Agriculture and Agri-Food Canada (AAFC) to form a Social Science Research Network on Sustainable Agriculture in a Net-Zero Economy, and Common Ground, with its focus on relations and relationships, was the winning proposal.
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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.059 | 0.005 |
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".