Indicators of community economic development through mural‐based tourism
Bibliographic record
Abstract
Community Economic Development (CED) has become a recognised form of economic development, despite contention regarding its definition and applications. It is acknowledged that development planning benefits from a more holistic approach with a focus on community participation. The objective of this paper was to explore the process and selected indicators of CED success through an examination of five Saskatchewan communities that have made the conscious decision to develop tourism through the use of wall murals on the exteriors of buildings. Extensive in‐person interviews were conducted with stakeholders in each of these communities. Generally, this research has found that both the CED process undertaken and the measurement of success are dependent upon the desired outcomes of mural development. For example, in communities that created murals‐as‐community‐beautification, the process was less formalised and success was measured more qualitatively, for example in increased community pride and the development of social relationships. For those communities where murals were developed as part of an explicit economic development strategy, the process was more formalised and the outcomes measured more quantitatively, including the numbers of visitors, employment and businesses created. This research also indicates that particular attributes of rural places play a critical role in how CED is understood, defined and carried out, and how successes, both tangible and intangible, are measured.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| 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".