Bibliographic record
Abstract
Fair trade, which has now made its way into the aisles of the retail giants in Europe and North America, started in a car trunk. While coffee is the commodity most famous for blazing the way for fair trade, it was, in fact, a latecomer. The first ‘fair trade’ product was lace from a Puerto Rican sewing circle, transported to Akron, Ohio, in Edna Ruth Byler’s car trunk in 1946 (Fair Trade Federation n.d.). 1 Mrs Byler was an active member of the Mennonite Central Committee (MCC), a relief, service, and peace agency, and her car-trunk marketing of the products of impoverished Southern producers soon expanded to encompass wood carvings from Haiti and lace from Palestinian refugees. By 1968, this initiative had developed into the MCC’s SELFHELP crafts, which opened its first shop in 1972 (Ten Thousand Villages USA n.d.). Its descendents are the dozens of outlets of Ten Thousand Villages that dot the upper-scale shopping streets of North America (there are about 150 retail outlets in the United States and another 50 shops in Canada). Any one of these stores offers crafts from dozens of countries, and they sell about $17.5 million of handmade crafts in Canada and $25 million in the United States (Ten Thousand Villages Canada 2011; Ten Thousand Villages USA 2011). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.363 | 0.154 |
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".