Avoiding Potential Traps in Fair Trade Marketing: A Social Representation Perspective
Bibliographic record
Abstract
Because fair trade (FT) is a new and complex phenomenon, it needs to be appropriated, through a process of symbolic coping, by the members of a community in order for their behaviours to be affected. This symbolic coping is done through, and can be understood via, the production of social representations (SRs). SRs arise out of the need to make familiar those objects and phenomena that are uncommon. Whenever a new phenomenon such as FT permeates a social group, it inevitably passes through a process of appropriation. This process is by no means neutral; on the contrary, it draws on the ensemble of images, ideas and connotations already present in a population. This article first provides a detailed analysis of the phases of the FT appropriation process based on Social Representations Theory (SRT). The article then assesses the potential traps or risks for the future of FT related to the circulation of information and the creation of social knowledge about FT among populations in the North.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.064 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.005 |
| 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".