Agritourism as a Solution to Rural Revitalization: The Case Study of Brock, Ontario
Bibliographic record
Abstract
The agricultural and agri-food industry has a prevalent, long-standing history as a successful cornerstone industry in Ontario. This historical prevalence stands true for the central rural township of Brock, Ontario as agriculture and livestock are a driving force in the township’s economy. However, throughout the last three decades, agricultural production in Ontario’s rural townships have been negatively impacted by vertical integration, globalization, and the intensification of land-based activities (Wicks & Merrett, 2003). Therefore, the aim of this research study is to determine if agritourism is a viable solution to aid in revitalizing the rural township of Brock, Ontario. This research is imperative for farm operators in rural Ontario because small family farms are seeking ways to remain economically competitive against the pressures of urbanization and changing global markets. The objectives of this research study necessitate in-depth information-gathering which will be gained from conducting semi-structured interviews with individuals whom are members of the Beaverton Agricultural Society, the Sunderland Agricultural Society, or the Ontario Federation of Agriculture (OFA). This research will provide information about why farmers decided to diversify their farms through agritourism, how agritourism has helped farming businesses (if at all), and what strategies farmers used to implement agritourism into their traditional farming practices. The findings of this research study will be utilized by for farm operators that have integrated agritourism practices into their traditional farming practices and for government programs and policies to support farm operators.
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 | 0.001 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".