Bibliographic record
Abstract
Ontario has seen a long history of changes and ownership dynamics since European settlers arrived on the continent in the 16th century. Natural resources, like wood, were already important to Europeans, and the vast forested landscape of Ontario provided opportunities for commerce and colonization. Widespread settlement in southern Ontario led to mass deforestation, while the pursuit of White pine in central Ontario nearly led to its eradication. At the turn of the 20th century, recognition amongst foresters and concerned members of the public for the need for better logging practises began. This became the basis for policies and legislation in the 1940s for Crown land in the north of the province. However, provincial legislature to protect and preserve private forested lands in the central and southern parts of the province went unaddressed until 1966 with the Woodlands Improvement Act. Nevertheless, private forest landowners continued to feel financial concerns related to property assessment and fair taxation, which led to two other programs being created. The Managed Forest Tax Rebate and Managed Forest Tax Incentive programs were introduced in 1975 and 1997, respectively. The archive at Haliburton Forest and Wild Life Reserve Ltd. holds interesting documents, and exchanges between the former owner and manager of Haliburton Forest, Peter Schleifenbaum, and the government, forestry organizations, and other concerned private forest landowners. The following paper will use these archival documents and other sources to look at how taxation of private forested lands has been addressed and changed over time, as well as Peter Schleifenbaum, and Haliburton Forest’s, role in these changes. The success of the current MFTI program stems from the willingness of private landowners to manage their forests sustainably, the fiscal benefits, and the adaptability of the program’s requirements to participate.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".