Eating Animals to Build Rapport: Conducting Research as Vegans or Vegetarians
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it