ADOPTION OF A WILDLIFE CONSERVATION PLAN BY CROP AND LIVESTOCK FARMS IN CANADA: WHAT FARMER AND FARM CHARACTERISTICS MAKE A DIFFERENCE?
Bibliographic record
Abstract
Management Survey (2001) conducted by Statistics Canadaand Agriculture and Agri-FoodCanada. The target population consists of 21,000 active farms in Canadawith sales greater than $10,000. The farms responded to the survey (N t = 16,053 with 76.4% response rate) were classified into three major categories: (1) “crop farms” (N c = 5,425), (2) “livestock farms” (N l = 2,250) and (3) “mixed farms” (N m = 8,378) with both crops and livestock. The results indicate that rate of This paper examines the impact of various farmer and farm characteristics on the adoption of a Wildlife Conservation Plan (WCP) – “a formal written document prepared by an expert that describes the measures to be taken by an agricultural operation to conserve natural land and wildlife habitants adjacent to it” - by crop and livestock farms in Canada. Those characteristics considered in the analysis include: human capital (age, sex), financial (profits, non-farm income, farm assets), farm structure (size, ownership), and social (degree of urbanization, population density). It uses data collected in the Farm Environmental adoption of WCP is comparatively less (13.9%) as compared to others, including manure, fertilizer, pesticide, water, and grazing management plans. The results from a Logit Regression analyses suggest that age, profitability, farm size, and degree of urbanization affect significantly on this behaviour in all farm types, however with varied size and signs. It highlights the importance of taking into account of voluntarily private-action of the farming community to formulate public-regulation aiming an environmentally friendly and conservative agriculture farm setting.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".