Eating Animals to Build Rapport: Conducting Research as Vegans or Vegetarians
Bibliographic record
Abstract
Notions of hospitality, community, and the fostering of rapport and connection are foundational concerns for conducting research across difference. Drawing on methodological literature, this paper considers how access to various communities and “good” data is structured by the notion that in order to develop rapport researchers accept the “food”, specifically “meat” offered by their hosts. When researchers are vegetarians or vegans, this can entail a conflict in which questions of hospitality, relationships, and responsibility to ethical commitments come to the fore. As such, we analyze methodological literature in which the logic of nonhuman animal sacrifice is considered a means to the ends of research through the development of “rapport”—often coded as an ethical relationship of respect to the participant. We draw on experiences of veg*n researchers to explore how this assumption functions to position the consumption of meat as a necessary undertaking when conducting research, and in turn, denies nonhuman animal subjecthood. We interrogate the assumption that culture and communities are static inasmuch as this literature suggests ways to enter and exit spaces leaving minimal impact, and that posits participants will not trust researchers nor understand their decisions against eating nonhuman animals. We argue that because food consumption is figured as a private and individual choice, animals are not considered subjects in research. Thus, we articulate a means to consider vegan and/or vegetarians politics, not as a marker of difference, but as an attempt to engage in ethical relationships with nonhuman animals. In so doing, we call for the inclusion of nonhuman animals in relationships of hospitality, and thereby attempt to politicize the practice of food consumption while conducting research.
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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 | 0.054 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.042 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".