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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".