Is local produce more expensive? Challenging perceptions of price in local food systems
Bibliographic record
Abstract
This research examines price in local food systems to identify whether the perception that local is more expensive is justified. This study seeks to contribute to the field by addressing the dearth of quantitative price and availability research and building upon existing empirical research by considering a broader range of distribution channels and organic produce. Without a stronger understanding of pricing structures and distribution models, local food initiatives are based on assumptions rather than evidence. Using a case-study approach of the Region of Waterloo (Ontario, Canada), price and product data were collected at 11 outlets over a 6-month period. The study involved regression analysis of six locally produced fruits and vegetables based on local, Ontario, and organic attributes associated with the products and comparison with consumer willingness-to-pay research. Results show that local produce in the case study is not consistently more expensive than the non-local option. Both price discounts and premiums are found, depending on the product. These findings challenge the “local is more expensive” assumption and support suggestions that local food systems can be spaces for social inclusion. The organic attribute is associated with a price premium in all cases and may create confusion among consumers given frequent overlap between the local and organic attributes. Proponents of local food can use the results of this study to inform programme and policy development. Most notably, the study suggests that education around the distinction between local and organic as well as challenges to the price perception could be of benefit.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".