The Impact of Gaming on Rural Heritage Communities: A Case Study of Elora, Ontario
Bibliographic record
Abstract
Since the early 1990s, rural decline has led many communities to begin social and economic restructuring. Several locales are seeking alternative approaches to the primary sector to support their declining industrial base (Markey et al, 2008). The tourism industry is an alternative to traditional rural livelihoods. Rural amenities and scenic landscapes have encouraged stakeholders to develop heritage tourism. \n The commodification of heritage has a profound impact on the place identity of rural landscapes. This is illustrated in the Model of Creative Destruction. In an earlier paper, Mitchell (1998) described the process of creative destruction through 5 stages being early commodification, advanced commodification, early destruction, advanced destruction and post destruction. In later papers, Mitchell and Vanderwerf (2010) describe the model as one that predicts that rural landscapes may evolve through three identities; rural town-scape, heritage-scape (or heritage village) and leisure-scape. Communities will remain as heritage-scapes if the desire to preserve is a dominant motivation. In contrast, if stakeholders are motivated more by a desire to profit or promote economic growth, then investments in non-conforming venues may result. This ultimately will shift the identity from one of heritage-scape to leisure-scape of mass consumption. Such investments may jeopardize a tourist’s heritage-seeking experience, and their perception of the community as a heritage village. \n Gaming recently has been introduced as a form of rural economic development in communities that commodify heritage (i.e. heritage-scapes). The introduction of slot machine parlours at racetracks (racinos) has helped combat the decline in the horse racing industry (Thalheimer and Ali, 2008). Furthermore, the positive economic impacts of these facilities are numerous. Negative implications, however, also accompany this type of tourism development. To date, little research has been conducted on the impacts that racino gaming developments have on communities, and, more specifically, on heritage-scapes. This thesis seeks to address this deficiency in a case study of Elora, Ontario and the Grand River Raceway. \n The purpose of this study was i) to determine the impact of the Grand River Raceway on Elora’s identity as a heritage village; ii) to identify the positive and negative socio-economic benefits that the facility has on the community and iii) to provide recommendations to communities who are considering similar development. To meet these objectives, data were collected through business and tourist surveys, unstructured interviews and a content analysis of secondary sources. \nResults suggest that the Grand River Raceway has not compromised Elora’s identity as a heritage-scape, in the eyes of business owners and tourists. Although the presence of the Grand River Raceway suggests that Elora is at the stage of early destruction or is on the way to becoming a leisure-scape, its presence has not detracted from visitor experience, as predicted by the model. This situation is attributed to marketing, location and uniformity with the existing landscape. \nFurthermore, the Grand River Raceway has had both positive and negative socio-economic impacts on Elora. Some of the benefits include employment, tax revenues, sponsorships and financial contributions to the municipality. At the same time, however, the Grand River Raceway has created a divided community, generated several legal issues and resulted in an uneven distribution of economic benefits. It is recommended that public consultation and resident involvement in decision making will help to minimize these negative impacts.
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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 | 0.002 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".