Bibliographic record
Abstract
Purpose This paper critically examines the marketing of fair trade, arguing that the use of the term producer conflates a number of categories of actors, not all of whom benefit equally. The authors contend that the two existing archetypes – the noble peasant farmer and the independent artisan – and the emerging archetype of the “empowered decision maker” serve to obscure and mask complex labour relationships. Design/methodology/approach This conceptual paper draws on a wide range of literature and original fieldwork conducted by the authors to illustrate the three marketing archetypes in the fair trade value chain. Findings Hidden behind the three dominant archetypes used to promote fair trade is a relationship between fair trade “producers” (small farmer, craft enterprise and plantations) and permanent and temporary/casual labourers. The trickle‐down of fair trade benefits to these workers is uneven at best and falls far short of the expectation of empowerment of all “producers” that fair trade promises. Research limitations/implications The fair trade project must look beyond the simple archetypes to engage more deeply with labour issues in the fair trade value chain, and to re‐engage with fair trade as a development strategy through which broader and more complex forms of empowerment can be realised. Practical implications Fair trade standards are not a substitute for organised labour's activities. Interactions between trade unions and fair trade bodies could ensure that existing labour standards are met, and improvements in the lives of all workers can occur. Originality/value This paper conceptualises three fair trade mainstream marketing archetypes and suggests why and how the fair trade movement must move beyond these to ensure empowerment amongst its least well‐off stakeholders.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.043 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".