Legible Landscapes: Incentivizing Forest Knowledge and Action in Southern Ontario
Bibliographic record
Abstract
Abstract Background This paper traces the changing dynamics of forest management on privately owned land in southern Ontario, Canada, using the conceptual lens of state legibility to highlight how incentive programs are creating new ways of seeing and engaging in stewardship. Specifically, the Managed Forest Tax Incentive Program (MFTIP) and its corresponding Managed Forest Plan are investigated as a means through which a diversified field of knowledge has been activated to enable climate-conscious adaptive stewardship across the region. Methods This case study uses a qualitative approach, incorporating document analysis, semi-structured interviews, and direct observation. Similar patterns and relationships within and across sites are identified to build theory and shed light on the socio-ecological context of private forest management. Results Set within southern Ontario’s history of forest management and the rise of neoliberal environmental governance, this paper contributes theoretically to scholarship on state legibility. The results illustrate a shift in stewardship on private lands through a rescaling of management responsibility that embraces different perspectives and builds place-based practical knowledge of forest systems. By mapping and building knowledge networks, diverse approaches to management have proliferated at the local and regional levels. These approaches have been influenced by previous management experience, different professional backgrounds, knowledge of participants, and the motivation of landowners to engage in active stewardship. Conclusion The process of developing a management plan plays a key role in making landscapes legible to all stakeholders. The document also serves as an instrument of the state to build private landowners’ and forest consultants’ knowledge and capacity. This has set in motion a socio-ecological landscape strategy to address encroachment, invasive species, and climatic challenges in this increasingly urbanizing region.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".