Bibliographic record
Abstract
This MRP addresses the rhetoric used in regard to veganism by analyzing comments made within forums on the social media platform Reddit. It focuses on analyzing the rhetoric used by individuals who follow a vegan diet, as well as the response rhetoric from those who are anti-vegan and/or do not follow a vegan diet. This MRP also addresses the stigma present towards vegans and veganism as a whole. In addition, this MRP discusses why social media is being used to investigate vegan rhetoric and what strategies both sides of the veganism debate use to advocate their viewpoint. The objective of this MRP is to examine the normalization and stigmatization of veganism online as well as the role that the rhetoric surrounding veganism plays for both vegans and non-vegans on social media. The literature review addresses the overarching themes of vegan rhetoric, with a focus on the differing rhetoric used by vegans and non-vegans. Communication Accommodation Theory (CAT) was used as a theoretical framework for addressing the research questions. The study explores the normalization and stigmatization of veganism online and examines the potential for rhetorical consistencies and patterns that can be found within the rhetoric surrounding veganism on an online forum. The findings reveal that veganism is both stigmatized and normalized online. The analyses demonstrate that veganism is stigmatized more than it is normalized. Rhetorical consistencies and patterns were found to be commonly used by both parties to support their position in the veganism debate including strategies involving environmental, health, and ethical rhetoric. In future studies, it would be of interest to expand the data collection in order to find evolving keywords and patterns surrounding online vegan rhetoric.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".