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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.031 |
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; both teacher heads agree on what is shown here.
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".