Bibliographic record
Abstract
One of the areas with great potential for economic, social and environmental benefit is community-based retailing. The concept of community based retailing can incorporate a number of different tenets. We suggest that it is retailing that is based close to the community it serves, usually within the town or village centre rather than out-of-town locations, and which is composed of a diverse range of small and medium sized business that are often independently or co-operatively owned. These community-based businesses form part of the social and community infrastructure.\nWe first of all explore the broad benefits of community-based retailing. We focus on local food where local food economies in combination with community-based retailing can help to combat ‘food desserts’, areas (usually low income areas) with little or no access to affordable, nutritious food. We examine the, at times, vexed role of multiples in community-based retailing, and consider the future, in particular retailing and sustainability issues. The retailer is perceived as a key gatekeeper or catalyst in achieving sustainability outcomes for a wide range of stakeholders. If the retailer adopts sustainability practices, his customers, as well as his suppliers, will be encouraged, or have little choice but, to follow suit.
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 | 0.004 | 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 | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".