Overcoming the local trap through inclusive and multi-scalar food systems
Bibliographic record
Abstract
It has long been shown that industrialised food systems have harmful consequences for people and the planet. Relocalising food systems is one strategy to mitigate these harms and advocates point to resulting ecological, economic, and social benefits. However, when the local is assumed to be inherently preferable to the other scales, food system actors can fall into what has been identified as the local trap. Such understanding of local can translate into defensive and exclusionary tendencies towards the food preferences and practices of those considered “non-local”, such as immigrants. While the literature identifies various manifestations of the local trap, it offers limited investigation of strategies to overcome this pitfall. In this article, we identify strategies that include the food preferences and practices of newcomers while also addressing problematic aspects of industrial food systems. We also seek to understand the mechanisms and conceptualisations that enable such strategies. We first present a conceptual framework for inclusive and multi-scalar food systems based on an extensive literature analysis. In contrast to defensive localism, alternative conceptualisations of scale may support action in favour of collaborative, inclusive, and diversity-receptive outcomes in food systems. Second, we apply this newly created framework in an empirical study of food practices and goals of the EthniCity Catering program in Calgary, Canada to illustrate the potential application of such strategies in a specific time and place. With this, the article makes not only a theoretical contribution to the geographical scale debate in and beyond food studies but also shows practical implications.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.034 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".