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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".