Bibliographic record
Abstract
This paper contends that eating shapes the self; that is, our practices and understandings of eating can cultivate, reinforce, or diminish important aspects of the self, including agency, values, capacities, affects, and self-understandings. I argue that these self-shaping effects should be included in our ethical analyses and evaluations of eating. I make a case for this claim through an analysis and critique of the hypothesis that young women’s vegetarianism is a risk, sign, or “cover” for eating disorders or disordered eating. After outlining the relevant empirical literature, I suggest that the evidence for this hypothesis is inconclusive. Given this uncertainty, we should consider the risks of making a mistake when accepting or rejecting this understanding of young women’s eating. I argue that these risks importantly include negative effects on the self, such as damage to moral and epistemic agency. Along with other potential consequences of mistakenly accepting the hypothesis, these effects give us reason to reject it pending more conclusive research. Overall, this paper offers a philosophical intervention into the debate over the relationship between vegetarianism and eating disorders while illustrating the ethical importance and relevance of eating as a self-shaping activity.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.031 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".