Looking beyond the digital veil: an investigation of the (de)commodification of three “Vietnamese spices”
Bibliographic record
Abstract
The contemporary global spice trade is a multi-million dollar industry that frequently relies on Global North consumers’ romantic visions of spices and their cultivators in the Global South. From fieldwork with ethnic minority farmers in upland northern Vietnam growing star anise, Cinnamomum cassia (often marketed as cinnamon), and black cardamom, and from a content analysis of digital marketing websites, it becomes clear that astute practices of commodification and de-commodification are invoked at different nodes along these spice global commodity chains. In this paper we investigate the strategies deployed by Vietnamese state officials, Vietnam-based exporting companies, and overseas importing and retail companies to promote and market these three spices. We find major disjunctures between the “geographical indications” approach advanced by the Vietnamese state to link products to particular places and peoples, and the “placeless” strategies mobilized by private Vietnamese and Chinese exporters. Global North importers further complicate the story, often attempting to de-commodify or de-fetishize the spices on digital-marketing platforms. By focusing on the final nodes along these commodity chains – yet to be studied or critiqued – our findings raise important questions regarding the implications of such divergent marketing strategies for farmers at the initial nodes of these spice chains.
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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 | 0.002 | 0.003 |
| 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 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".