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Cited by 1,066

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Avoiding the Local Trap: Scale and Food Systems in Planning Research

Born, Branden, Purcell, Mark·2006·Journal of Planning Education and Research

Abstract

A strong current of food-systems research holds that local food systems are preferable to systems at larger scales. Many assume that eating local food is more ecologically sustainable and socially just. We term this the local trap and argue strongly against it. We draw on current scale theory in political and economic geography to argue that local food systems are no more likely to be sustainable or just than systems at other scales. The theory argues that scale is socially produced: scales (and their interrelations) are not independent entities with inherent qualities but strategies pursued by social actors with a particular agenda. It is the content of that agenda, not the scales themselves, that produces outcomes such as sustainability or justice. As planners move increasingly into food-systems research, we argue it is critical to avoid the local trap. The article’s theoretical approach to scale offers one way to do so.

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Metadata

Authors
Born, B., & Purcell, M.
Venue
Journal of Planning Education and Research
Published
2006-12
Volume
26
Issue
2
Pages
195-207
ISSN
  • 0739-456X (ISSN-L)
  • 1552-6577
OpenAlex
W2104460807
Cited by
1,066
References
2
FWCI
89.87
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Avoiding the Local Trap: Scale and Food Systems in Planning Research. https://app.cochat.ai/papers/10.1177/0739456x06291389
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Born, B., & Purcell, M. (2006). Avoiding the Local Trap: Scale and Food Systems in Planning Research. Journal of Planning Education and Research. https://doi.org/10.1177/0739456x06291389

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.