Exploring Constituents of Short Food Supply Chains
Bibliographic record
Abstract
By deploying a systematic review approach, this chapter provides a holistic exploration of AFNs which contributes to further mobilization of locally produced products. This chapter explores the constituents of AFNs by studying food citizenship, sustainability and food democracy, food safety and quality, embeddedness and social capital, the relationship between the level of participation in AFNs and consumers’ demographics, consumers’ motivations to engage in buy-local activities, vendors’ perspective on selling products in farmers’ market, and the development of short food supply chains in the Canadian context. Specifically, the social interaction aspect of buying local, for example, engaging with vendors and other consumers, has been cited as a factor that motivates consumers to buy local food products from the farmers’ market; however, consumers had to deploy online ordering channels with door delivery option during COVID-19 pandemic to access locally produced products safely. To capture one aspect of the potential impacts of COVID-19 pandemic on AFNs, future research can explore whether social interaction is still an influential factor in consumers decision to buy local, or the importance of the social interaction aspect of buying local will be replaced by the convenience of receiving the fresh, locally produced food products at consumers’ doorstep via online ordering process.
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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 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".