Solidarity™: Student Activism, Affective Labor, and the Fair Trade Campaign in the United States
Bibliographic record
Abstract
Abstract: Ethical labeling campaigns have become a central means for diffusing and negotiating conflicts between social movements and market actors. Fair Trade was a pioneering ethical label and, by many accounts, a success. For nearly a decade, United Students for Fair Trade (USFT) activists worked to build the reputation of the Fair Trade Certified (FTC) label, but in Fall 2011 they withdrew their support and urged ethical consumers to do the same. This paper is an urgent reflection on USFT's trajectory from guerrilla marketing to boycotting FTC products. While their actions may appear shortsighted and contradictory, their decision to withdraw support from the FTC label has roots in a long struggle for control of Fair Trade. We argue that their actions signal a new stage in consumer action, as activists lose faith in the legitimacy of ethical labels and instead target the agencies that own the ethical brandscape.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".