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Record W4200393789 · doi:10.1080/15528014.2021.2015666

Looking beyond the digital veil: an investigation of the (de)commodification of three “Vietnamese spices”

2021· article· en· W4200393789 on OpenAlexafffund
Sarah Turner, Celia Zuberec, Michelle Kee

Bibliographic record

VenueFood Culture & Society · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCommodificationVietnameseArtAestheticsPhilosophyLinguisticsEconomicsEconomy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score0.218

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.203
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

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