Placing the 'natural' edges of a metropolitan region through multiple residency: landscape and urban form in Toronto's 'cottage country'
Bibliographic record
Abstract
This study examines certain 'cottage' or water-oriented second-home settings of central Ontario to assert that they have effectively become part of the Greater Toronto Area (GTA), a major metropolitan region now undergoing rapid population growth. The central thesis is that this so-called 'cottage country' must be considered part of the primary life-space for many individuals and households based in the GTA. Multiple residency---the social-spatial practice by which households live in more than one dwelling---is examined to make sense of what now comprises the Toronto-centred urban territory or 'metapolis' and its housing markets, while also enabling us to 'place' the 'natural' edges of this metropolitan area in at least two important ways. It first helps to demonstrate certain spatial qualities of the GTA as an unevenly urban territory. At the same time, the waterfront components of 'cottage country' are 'living edges' in landscape ecology terms and significant sites or 'places' that enable individuals and households to situate themselves within abstract notions of 'nature' and the 'wilderness'---ideas about land and landscape that have long held sway in Canadian cultural discourses. The mixed quantitative and qualitative methodology employed here includes a generalised social history, a detailed questionnaire (n=200), and in-depth interviews with cottage users (n=30) in three discrete second-home settings. These case study areas are situated within broader discourses and processes of transformation, exploring certain dynamics of urban form, structure, and metropolitan growth while also examining important dimensions of how people think about space, place, landscape, and what has been called the 'sense of region'---all of which are arguably revealed by 'cottaging' as a culturally meaningful social practice. Conceptually, the research presented here is thus a dialogue between markets and meaning. Beyond its empirical contributions, this study is intended to assert the importance of an epistemological approach to landscape and urban form---the domains of cultural and urban geography, respectively---in concert. Such an approach is needed if we are to substantively examine abstract processes, narratives, and/or conceptualisations of space and landscape without neglecting to systematically ground them in place and in the materiality of urban form.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".