‘The Gift that Keeps on Giving’: Unveiling the Paradoxes of Fair Trade Shea Butter
Bibliographic record
Abstract
Abstract Fair trade has garnered the interest of western consumers seeking ‘ethical’ products. Inherent to the movement's success are narratives that foster an attachment between reflexive consumers and the distantly situated producers of their goods. These narratives entice consumers to purchase higher priced, quality fair trade products. Drawing on empirical evidence from a case‐study of fair trade shea butter produced by women inBurkinaFaso and consumed byEuropean andNorthAmerican women, this article considers the extent to which these narratives reflect producer experiences. Fair trade shea butter is depicted as an exotic, traditional, authentic, and ethical product that promotes global female solidarity. Yet, the international commodity differs significantly from the traditional and authentic product that is locally produced and traded. Moreover, the low returns butter producers earn for their product cast doubts on the ‘fairness’ and solidarity aspects of the movement. Hence, we argue that rather than unveiling the conditions of commodity production, fair trade discourses repackage these to appeal to ethically minded consumers likely to purchase fair trade goods.
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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.064 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".