Urbanites Creating New Ruralities: Reflections on Social Action and Struggle in the Greater Toronto Area." The Great Lakes Geographer 7
Bibliographic record
Abstract
As urbanites move in large numbers into the countryside, particularly adjacent to major urban centres, they transform the countryside, themselves and the political context. New ruralities are emerging in a variety of locations. In the rural-urban fringe and the urban shadow, affluent in-migrants have found their idyll to be flawed or blocked, and have taken action to ma ke their vision of the countryside become true. Struggles over social infrastructure, amenities, privacy and a myriad of other issues have clarified the exurbanites new ruralite visions, often with dramatic restructuring of formerly rural areas into those new ruralities. Exurban ite ruralites in the T oronto cou ntryside, the G reater Toron to Area and the Toronto Census Metropolitan Area, have been established for a generation. Struggles over waste-dump location, followed by a myriad of smaller struggles have been part of a re-creation of the rural environment and its political surround. This creation of new rurality by exurbanites, through struggle, is the focus of this paper. Keyw ords: rura l-urban fring e, rural idyll, To ronto As urbanites move in large numbers into the countryside, particularly adjacent to major urban centres, they transform that countryside, themselves and the political context. New ruralities are emerging in a variety of locations. In the rural-urban fringe and
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.033 | 0.024 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".