Bastard Spice or Champagne of Cinnamon? Conflicting Value Creations along Cinnamon Commodity Chains in Northern Vietnam
Bibliographic record
Abstract
ABSTRACT In upland northern Vietnam ethnic minority farmers are cultivating what some global retailers refer to as the ‘champagne of cinnamon’. However, a closer examination reveals that this spice is not ‘true cinnamon’ but cassia, with the exact species remaining uncertain. Drawing on commodity chain literature and debates over the creation of value and quality, the aims of this article are twofold. First, it investigates the making of ‘Vietnamese cinnamon’ as it moves from the hills of northern Vietnam to supermarket shelves in the global North, and the actors and livelihoods involved. Second, it explores how different actors define ‘Vietnamese cinnamon’ and infuse it with often‐contradictory values. Based on multi‐sited ethnographic fieldwork over a four‐year period, the study finds that the state and cooperating non‐government organizations tend to ignore ongoing taxonomic confusion while creating a geographical indicator to highlight the uniqueness of this commodity. Yet, concurrently, exporters and retailers in the global North focus on other distinctions as key marketing tools including remoteness, ethnicity, taste and health benefits. The article thus calls for an expanded analytical focus on competing value creation for agro‐food products and on the impacts for commodity producers, in this case ethnic minority farmers in the global South.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".