Russian Views on “Ecologically Clean ” Food: Basing Beliefs About Health on Personal Connections to Food Production
Bibliographic record
Abstract
or Chechnya seemed to take a back seat to worries about imported food and, implicitly and explicitly, imported capitalist influences. Slowly, I realized that concerns about food are a powerful means of criticizing global capitalism without appearing to invoke political and military nationalism. In this paper, I will examine how Russians think about, talk about and act about their food. In particular, I will explore what the phenomenon of “ecologically clean ” food means to Russians and how it informs Russian health and economic practices. The Russian phrase “ecologically clean ” differs from the English word “organic ” in a particular way: it refers to the human relationships that go into food production more than the physical processes of agriculture. Local food grown in the countryside, by friends and relatives, is considered the most healthy and most clean, whereas imported food and food produced for a profit is the most dirty and unhealthy. Unlike in Canada, such home-grown “dacha ” food provides the majority of food consumed by the Russian population. Economic motives are only part of the story; many Russians are eschewing packaged food out of health concerns. Contrary to popular western
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".