New directions?: the role of landowner and forestry consultant values in Ontario's private woodland management
Bibliographic record
Abstract
The conservation of privately owned woodlots must be a key component of any attempt to preserve and protect Ontario's wooded landscape, particularly in the southern portions of the province. As a result of the Managed Forest Tax Incentive Program much of the province's conservation effort has become dependent on the relationships between private landowners and the forestry consultants they hire to design management plans for their woodlots. If woodlot conservation is to be successful, a necessary first step is to develop a clear understanding of the attitudes and values that both groups have towards woodlot management and how this relationship is expressed in management plans. Research into the application of these values shows that while there are a few significant areas of departure, forestry consultants are designing management plans that are generally consistent with landowner needs and values. Additionally, given the range of values that have been incorporated into management plans, it can be speculated that a low majority of woodlot management in Ontario is holistic in nature, a fact that bodes well for the ecological health of these lands. Landowner interest in conservation, however, although strongly rooted in personal interest is also driven by tax and other financial rewards for such behaviours, which suggests that if reward programs such as the Managed Forest Program were to erode, the future of Ontario's woodlots might look significantly different.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".