Overcoming the local trap through inclusive and multi-scalar food systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it