Navigating Challenge and Change - The State of Rural Ontario
Bibliographic record
Abstract
The COVID-19 pandemic illuminated many challenges, changes, and opportunities for rural Ontario. It emphasized the significance of major trends and issues for rural Ontario, such as the critical nature of infrastructure, the vulnerabilities presented by increasingly tourism-dependent economies, the barriers rural people face in accessing critical health and social services, and accelerated changes to rural demographics and workforces. This poster showcases two resilience cases from rural Ontario: to Indigenize water governance by the Mississaugas of the Credit First Nation and re-localization of food systems in northern Ontario. The cases offer insights into the ways different communities are navigating challenges and change, and they share lessons for interrupting the dominant narratives about rural contexts through building local capacity to exercise community agency. In both cases, specific interventions based on community values, needs, goals, and aspirations serve to reclaim control from urban or outside powers, and re-embed both resources and decision-making processes in the hands of the people and places most affected by challenges, changes, and increasingly uncertain futures.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.014 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".