Local Buttons: Sustainable Fashion and Social Entrepreneurship in Haiti
Bibliographic record
Abstract
Apparel manufacturing is the largest contributor to Haiti’s export sector, employing about 30,000 people, mainly in the nation’s capital of Port-au-Prince. This number is only a fraction of those who were employed in the garment sector during its peak in the 1980s, before a trade embargo imposed by the Organization of American States devastated Haiti’s apparel industry. Factories that had supplied various brands and retailers in the United States relocated to other regions of the globe, never to reestablish operations in Haiti. Today, in a post-earthquake economy, apparel production is returning and preferential trade agreements with the United States have been implemented to encourage US businesses to select Haiti as a sourcing option. The export of newly manufactured clothing from Haiti for consumption in the marketplaces of developed economies (primarily the United States) stands in contrast to the secondhand clothing imports that are sold in Haiti’s various marketplaces for local consumption. Used clothing is often imported to Haiti from the same markets that receive its new clothing exports. Secondhand clothing is frequently redesigned to suit local taste and body types by skilled seamstresses and tailors, resulting in a secondary informal economy driven by the selling of used clothing. This article discusses a novel business model implemented by a Toronto-based fashion startup in 2010 in an effort to support Haiti’s apparel manufacturing sector and also make use of the abundant secondhand clothing found it its local markets. This model presents a potential sustainable solution to the volumes of used clothing filling the global marketplaces of developing economies, often at the expense of economic growth that would be strengthened by local consumption of domestically produced clothing.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".