The local contexts of meat consumption: analyzing meatification in Nigeria
Bibliographic record
Abstract
Although food consumption habits across the globe have taken different paths, agri-food scholars are now pointing to a narrowing of choices on a global scale due to globalization. Adopting a food regime theory framework that is tightly connected to globalization theory, several thematic frameworks are used to provide a time-place-actor analysis of food consumption habits, identifying global trends as well as the factors driving those trends. This article employs meatification as a thematic framework for analyzing trends in meat consumption in the context of the third food regime. It argues that while global actors and factor should be acknowledged, in certain local contexts, they can be influenced, shaped or even constrained by local currents. Therefore, it opens a conversation about the danger of discounting or ignoring local currents when applying these thematic frames in food regime discourse. It shows that while meatification is taking place, in Nigeria, unlike in many other parts of the global south, the actors are not fast-food restaurants and supermarket, but bukaterias and open stall meat sellers in the local markets.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".