Bibliographic record
Abstract
Despite its history, fermentation is currently celebrated as both a food and health intervention. In the context of healthist discourses, fermentation is no exception to the sticky reaches of singular food truths: eat this, not that, because “it’s good for you[r gut].” However, the mandate to eat fermented or probiotic foods requires time, know-how, and material resources that are not accessible to all who eat. This framing of fermentation also fails to account for the multiplicity of health needs and ignores other barriers to food/health access. By privileging self-reliance (e.g. “I don’t buy bread; I bake my own”) and control (over one’s body, over microbial life), fermentation practices enable a culinary stance of moral superiority, which reinforce a healthist paradigm that it claims to subvert. This paper examines healthist fermentation, or pursuing fermentation in the name of gut health, to problematize assumptions about choice and control in fermentation contexts. It argues that health is not a fixed state but rather enacted with more-than-human agencies and (re)negotiated at every eating event. Using a combination of approaches from critical food studies, feminist theories, and communication studies, this paper analyzes fermentation as a way to reimagine health as being co-constructed with other scales of life.
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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.088 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.007 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".