Bridging Agrarianism: The Potential of Value‐Added Craft Cider Production to Support Rural Livelihoods in the Pacific Northwest*
Bibliographic record
Abstract
Abstract Against the decades‐long trend of aging farmers and farmland consolidation in the United States and Canada, value‐added farm production has been pitched as a lifeline to provide viable rural livelihoods for younger generations. How do producers perceive the possibilities and limitations of value‐added craft production in supporting agrarian livelihoods? More broadly, how are contemporary structural constraints and cultural shifts shaping new agrarian strategies? This article draws on in‐depth interviews and ethnographic data with urban and farm‐based cidermakers in the Pacific Northwest (British Columbia, Washington State, and Oregon). I find that while craft cider has helped buffer some producers against the volatility of selling raw fruit to large commodity markets, the benefits of this niche market do not widely support continued primary production or farm succession. I underscore the emergence of a livelihood strategy I refer to as “bridging agrarianism” among young cidermakers who wish to maintain a connection to agriculture but are shifting away from full‐time farming due to lifestyle preferences and economic constraints. Bridging agrarianism is manifest in modest forms of on‐site production that carry great symbolic weight. This study provides insight into how current generations of agriculturalists are developing new strategic responses to the political‐economic challenges of farming.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".