Strengthening and sustaining a community through reciprocal support between local businesses and the community
Bibliographic record
Abstract
Purpose Community economic development (CED) focuses on the creation of sustainable communities. To that end, a reciprocal relationship that sustains the community and business alike can be created. However, little is known about the nature of informal interactions between residents and businesses that achieves that end. This study aims to explore the nature of these interactions and their contribution to CED within a rural context. Design/methodology/approach A case study approach was used with interviews with five rural entrepreneurs. Questions explored the nature of the support that they receive from their home community and their contributions back to it. Findings The results show that communities and businesses do not operate independently of each other, but rather are mutually supportive and contribute directly to the other’s objectives. These relationships are reinforced over time by a business owner’s direct involvement in the community, though this process takes time and effort. Research limitations/implications This study focuses on a limited geographical area in British Columbia with a small group of rural entrepreneurs. The results may not be generalizable to other contexts. Practical implications The results suggest concrete actions that both the rural entrepreneurs and their associated communities can take to be mutually supportive of each other to the benefit of each party alike. Originality/value This paper enlarges the understanding of the types of interactions, especially informal ones, that can support both businesses and the larger community in their efforts to sustain themselves and contribute to CED efforts.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".