Farmers’ ecological motivations: implications for forestation in South Nation River watershed and Ontario’s mixedwood plains ecozone
Bibliographic record
Abstract
This paper investigates deforestation in southern Ontario’s mixedwood plains ecozone. Farmers own much of the land in the mixedwood plains, thus forestation is examined through the lens of farmers’ ecological motivations. \nTwo research methods are employed: an integrative literature review, and key informant interviews of farmers in the South Nation River watershed. \nFirstly, farmers’ ecological motivations on forestation in the EU, USA, Canada and Australia are examined via an integrative literature review of peer reviewed research. Various themes and issues are explored, which differ by region, policy, economic regime, and biophysical conditions. \nSecondly, the South Nation River watershed in eastern Ontario is examined closely since it experiences accelerated deforestation in the early 21st century. Results of key informant interviews of farmers in the South Nation River watershed are presented and compared to literature review results, which are quite different. \nAre tree-cutting by-laws effective at preventing deforestation? Experiences from other southern Ontario municipalities are compared to key informants’ comments. Only one literature review article examined the role of regulations. \nA main driver of deforestation in southern Ontario is urbanization. The literature review contains few references to urbanization. Adjacent eastern North American regions are examined and compared to southern Ontario’s mixedwood plains. \nMotivational crowding out is a concern in several literature review articles. The effects of motivational crowding out on farmers’ intrinsic motivations are discussed when extrinsic conservation motivators are introduced. Motivational crowding out has consequences; programme design may minimize these.
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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 | 0.001 | 0.003 |
| 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 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".