Exploring recipes of (de)colonization: a scoping review of decolonization and food systems scholarship
Bibliographic record
Abstract
In response to the growing emphasis on ‘decolonizing’ food systems and the critical perspectives cautioning against the overuse of this term, we systematically review English-language, scholarly literature on food systems to explore how researchers conceptualize and apply decolonization. Using a qualitative coding approach, we analyzed 112 texts, and highlight broad trends in definitions, key themes, and contributing concepts. We find a significant growth in scholarship on decolonization and food systems, particularly since 2018, with most work appearing in social sciences and humanities journals. Our findings highlight the diverse and nuanced ways in which decolonization is interpreted and applied in food systems research. Definitions of decolonization vary widely, often implying a process of reclaiming Indigenous practices, resisting colonial systems, and pursuing broader social justice aims. Key themes include Indigenous food sovereignty, traditional dietary practices, botanical naming, land and water rights, environmental perspectives, academic decolonization, and social movements. We conclude by emphasizing the need for careful and intentional use of the term decolonization and advocate for greater engagement with Global South scholarship.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".