An Importance-Performance Analysis Of the Motivations Behind Agritourism and Other Farm Enterprise Developments in Canada
Bibliographic record
Abstract
Challenging conditions in the current agricultural context have encouraged farmers develop agritourism and other enterprises on their farmland. Previous research suggests that a complex set of personal and economic goals drive the creation and maintenance of agritourism and other on-farm diversification ventures. However, the extent which those goals are accomplished has not been verified. This study employs an importance-performance analysis (IPA) examine the level of accomplishment of different goals driving agritourism and on-farm entrepreneurial development in Canada. IPA shows that goals with high levels of both importance and accomplishment are to continue farming, to enhance personal/family quality of life, to increase or diversify the market, and to respond a market need or opportunity. Further, results show differences in goals between agritourism and other types of farm entrepreneurs. Study findings suggest that extension agents can focus on the operator goals considered be most important and yield higher levels of accomplishment as they promote agritourism and other farm enterprises. These results have important implications for rural well-being, as agritourism is suggested keep family farms economically feasible and revitalize local communities. Keywords: agritourism, farm enterprise diversification, rural tourism, importance-performance analysis, rural well-being, goal
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".