Agritourism as Rural Development andFarm Diversification: A Southwest Ontario Case Study
Bibliographic record
Abstract
The character of many Canadian rural regions is changing rapidly as, on one hand, many farms become larger, more specialized and more embedded in a global food system while at the same time (ex)urban actors increasingly influence rural economies with new expectations of rural spaces. The latter are creating new possibilities for those family farms who are seeking a different path than corporatization and intensification. Agritourism is one strategy used by small to medium sized farms to capitalize on these new possibilities while simultaneously remain within the agricultural sector. Considering this context, this research aims to understand and document the role of agritourism as a form of farm diversification and rural development in Essex County, Ontario. This was accomplished through a discourse analysis of the County’s 2016 agritourism-related marketing materials and semi-structured interviews conducted with agritourism farm owners, winery owners, and representatives of the destination marketing organization (DMO). The results highlight the many facets of collaborative tourism development between wineries, agritourism farms, and tourism organizations. Differentiated marketing exists between food, agriculture, and wine, which may reflect the active collaboration between winery owners and the DMO to co-create a wine destination and the tendency of many agritourism farms to work in silos and cultivate a personal niche without regard for regional branding or destination creation. However, noted convergences and divergences between marketing strategies and farmers’ perceptions of the role and services of agritourism indicate potential missed connections and opportunities for the developing tourism destination to leverage its local resources more fully.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".