Return migrants, mini‐tours and rural regeneration: A study of local food movement in Taiwan
Bibliographic record
Abstract
Abstract Since the turn of the century, food safety has been one of the most significant social issues in Taiwan. In face of a series of alarming food‐poisoning scares, which pushed many Taiwanese to search for good safe food, the Taiwanese authorities have initiated a variety of strategies to handle food production issues. At county level, government sponsored projects, such as rural regeneration projects, have coincided with a wave of return migration, in which young city dwellers have returned to rural towns in order to engage in sustainable food production. There is also a popularising trend of ‘mini‐tours’, a leisurely activity that sees urban tourists visiting rural regions in search of ‘authentic’ traditions, such as those around food. This confluence of food safety concerns, official rural regeneration schemes, civil movements for sustainable food production, local tourism and emerging discourses on authentic food and food localism has fashioned an interesting food scene in Taiwan. This paper will examine the impacts of these joint forces. It will also illustrate how local food producers have struggled to construct a new food producer–consumer relationship that also contributes to the sustainable development of Taiwan.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".