Essex County Agri-Tourism: Exploring Regional and Farm-Level Diversification
Bibliographic record
Abstract
The character of many Canadian rural regions is changing rapidly as farms continue to become larger and more specialized and new (ex)urban actors influence rural economies with new expectations of rural space. In Essex County, Ontario, this changing character is being directed, in part, through tourism promotion and by leveraging its location within a designated wine appellation. As tourism is being pushed to a more prominent position within the County’s brand, farmers are provided opportunities to capitalize on increased visitation and an evolving perception of the region. Considering this regional context, this research aims to explore the uses of and motivations behind agri-tourism in Essex County, Ontario by three stakeholder groups: farmers, winery owners, and the Destination Marketing Organization (DMO). To accomplish this, document analysis was conducted alongside interviews with agri-tourism providers and DMO representatives. The dominant narrative emerging from analysis is the development of an emerging tourism destination. While winery owners actively collaborate to co-create a wine destination with the help of the DMO, farmers who have diversified into agri-tourism provision are more likely to work in silos and cultivate a personal niche without regard for a regional brand or destination creation. Though tourism promotional materials emphasize local food and beverages, it is questionable if farmers are actively buying into or are aware of this potential tourism opportunity. This research contributes to an understanding of Canadian farm diversification, particularly within the context of an emerging tourism destination.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".