Urban Flight and Rural Rights in a Pandemic: Exploring Narratives of Place, Displacement, and “the Right to Be Rural” in the Context of COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic has exacerbated many preexisting challenges facing rural communities and brought tensions in rural–urban relations closer to the surface. This article offers an explorative contribution to discussions in critical geography by comparing media narratives surrounding urban flight to rural places during the COVID-19 pandemic. Comparing field sites in rural Michigan (United States), Ontario (Canada), and the “Atlantic bubble” (Canada), we use an emerging theorization of the “right to be rural” to explore how urban flight and rural displacement are tied to concepts of community, identity, and safety. This approach is grounded in the political economy of rurality and emphasizes the power relations, inequalities, and historical contingencies that structure the experiences of full-time and part-time rural residents during the pandemic. Our exploratory discussion surfaces critical tensions in the geographically and socioeconomically uneven implications of the pandemic, including the “anxious economic acquiescence” experienced in many tourism-dependent rural regions and both the “hard” and “soft” ways in which rural regions responded to increased demands for access. We argue that the political economy of rural–urban relations is critical to understanding the social processes that will shape the “right to be rural” during and after COVID-19.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.032 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".