Whose labor counts as craft? Terroir and farm workers in North American craft cider
Bibliographic record
Abstract
The production of craft hard cider has been pitched as a lifeline to small- and medium-scale apple producers in Canada and the United States, who are grappling with the pressures of global capitalism. A key marketing tool has been to foreground the unique geographical region in which the apples are produced and fermented into alcohol. While many craft cider producers are at an early stage of business development, some have expressed interest in geographical indication (GI). However, the viability of the craft cider industry remains largely dependent on racialized migrant workers who face considerable barriers to accessing basic rights and freedoms. Amid efforts to link craft cider to specific places and construct artisanal livelihoods as prestigious, how does the craft cider industry account for its dependence on workers who are not from those places and are employed in so-called bad jobs? To explore this question, I draw on interviews and participant observation with actors throughout the Canadian and US cider, apple and horticultural industry. I argue that there would be considerable logistical and cultural barriers to distributing material premiums and symbolic recognition from GI craft cider to farm workers in this context and that more fundamental policy changes are crucial.
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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 | 0.001 | 0.001 |
| 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 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".