Markets of the Heart: Weighing Economic and Ethical Values at Ten Thousand Villages
Bibliographic record
Abstract
Abstract Purpose This project explores tensions at the heart of the fair-trade organization Ten Thousand Villages. I investigate the ways in which this organization attempts to balance concerns of North American staff and volunteers, to care for artisans abroad, and to incorporate expansion plans in the face of challenges raised by the recession. Methodology/approach This chapter draws on fieldwork with stores in Toronto (2011–2012) and ongoing fieldwork (summer 2014 and 2015) with the flagship store in Ephrata, Pennsylvania. Findings Members express continuing tension between the organization’s founding Mennonite values and the more recent orientation chosen by leadership, to compete successfully in “regular” retail space against non-fair-trade brands. Store staff and volunteers perceive Villages’ buying practices, meant to provide “fairness” to producers in the developing world, as somewhat inconsistent with the treatment of North American store employees. Corporate leadership is mainly focused on ameliorating poverty abroad, rather than framing the organization’s work in a broader social justice context, which store staff and volunteers expect. Originality/value At a time of increasing dialogue about alternative value systems that expand notions of economic worth, the fair-trade movement offers a useful model for one attempt to work within the market system to ameliorate its damages. Understanding how one organization negotiates its own competing value systems can provide useful perspective on other revaluation projects.
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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 | 0.002 | 0.002 |
| 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 | 0.021 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".