Nature State: Incentivized Forests in Southern Ontario
Bibliographic record
Abstract
Nature State: Incentivized Forests in Southern Ontario investigates the rapid growth of voluntary private land conservation efforts in suburban and rural Ontario, focusing on the rise of incentivized management from the mid-1990s until present day. Using a mixed methods approach the study combines semi-structured interviews, archival research, and GIS analysis with case studies in southern Ontario. This research considers the coevolution of new taxation schemes for conservation, devolved governance, and privatized approaches to owning land and resources. In particular, this work examines the growing use of programs such as the Managed Forest Tax Incentive Program in order to manage environmental change and biodiversity of forested lands within an extended urban fabric. Incentivized environmental management raises important questions about the growing interdependence between suburban land conservation and urban housing affordability, the changing scales of stewardship, and the increasing role of finance in land conservation. My findings reveal the development of new actor assemblages and knowledge geographies that have come about due to the transfer of forest management activities from the state to landowners, the new spatialities of protected areas and their land use dynamics, as well as the integrated role of civil society and stewardship in addressing urban climate futures. Committee: Neil Brenner, Charles Waldheim, Sonja Dümpelmann
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".