Sustainability Planning and Collaboration in Rural Canada
Bibliographic record
Abstract
Rural communities, often the first indicators of economic downturns, play an important role in planning for development and sustainability. Increasingly, these communities are compelled to reimagine the paths that lead not only to economic success, but also to the cultural, social, environmental, and institutional pillars of sustainability. As the contributors to this volume demonstrate, there are many examples of such innovation and creativity, and many communities that seek out new ways to build the collaboration, capacity, and autonomy necessary to survive and flourish. Contributors: Don Alexander, Kirstine Baccar, Michael Barr, Mary A. Beckie, Moira J. Calder, Meredith Carter, Yolande E. Chan, Sean Connelly, Jon Corbett, Anthony Davis, Jeff A. Dixon, David J.A. Douglas, Roger Epp, Kelly Green, Lars K. Hallström, Greg Halseth, Casey Hamilton, Karen Houle, Glen T. Hvenegaard, Melanie Irvine, Bernie Jones, Robert Keenan, Rhonda Koster, Ryan Lane, Sean Markey, Shelly McMann, L. Jane McMillan, Morgan E. Moffitt, Karen Morrison, Karsten Mündel, Craig Pollett, Kerry Prosper, Mark Roseland, Laura Ryser, Claire Sanders, Jennifer Sumner, Kelly Vodden, Marc von der Gonna, Shayne Wright.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.027 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 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".