Reaping Nature's Dividends: The Neoliberalization and Gentrification of Nature on the Oak Ridges Moraine
Bibliographic record
Abstract
In this article, we follow the position that neoliberalism is not a state but a process of political, social, and economic development. We explore the neoliberalization of nature in the exurban region of a rapidly expanding metropolitan conurbation, the Greater Toronto Area in Ontario, Canada. Since 2001, the Oak Ridges Moraine legislation has been in place to halt urban sprawl and conserve the nature of a regionally significant landform. Our analysis suggests that the Oak Ridges Moraine legislation is consistent with neoliberalization but that the process needs to be seen in the context of half a century of rural and exurban gentrification. Longstanding class privilege is perpetuated through the aegis of legislation to preserve nature and protect the countryside. The legislation aestheticizes the Moraine as a unique landform, complements private-based conservation efforts, and voluntary policy initiatives, as well as marketizes the Moraine as a desirous place where wealthy residents reap nature's dividends. This analysis confirms the usefulness of neoliberalism as a concept, but suggests that it needs to be explored through a historically and place-based informed perspective. The case study also sheds light on nature's role in state action, and the rallying and shaping of a regional nature to support state power.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".