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Markets of the Heart: Weighing Economic and Ethical Values at Ten Thousand Villages

2017· book-chapter· en· W2742653927 on OpenAlexaboutno aff
Laurel Zwissler

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsnot available
Fundersnot available
KeywordsRecessionBalance (ability)Face (sociological concept)Economic growthPolitical scienceGeographyEconomicsSociologyMedicineSocial science

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.319
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.281
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations4
Published2017
Admission routes1
Has abstractyes

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