Social Responsibility and Community Development in Vermont’s Food Business
Bibliographic record
Abstract
Businesses are increasingly expected to contribute to community development and sustainability. This exploratory research examines how food and agriculturally-based Vermont businesses are defining the concept of social responsibility (SR), incorporating it into their enterprises, and linking their enterprises to their communities. We develop indicators of SR and use them to examine qualitative interviews of 20 food entrepreneurs. We find that these businesses expressed commitment to and claimed actions to contribute to a broad array of SR goals, including community (with specific mention of employee well-being and improved access to healthy foods), local economy, and the environment. In many cases the respondents cited measurable impacts their actions made such as employee retention, food access, improved farm nutrient management and support for and assistance to local businesses. Contrary to prior studies, firm age did not have a measurable impact on SR values or practices. However, we found evidence of a U-shaped relationship between SR and scale, where small and large firms were more highly engaged and medium scale ones slightly less so. Implications focus on strategies for improved metrics for validation of 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 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".