Bibliographic record
Abstract
Through content analysis of three relevant research essays, this study examines how vegan communities contribute to the reformation of the cultural identity of vegan-identifying persons. Jessica Greenebaum’s (2012) research on identity and authenticity studies the different ways in which people classify themselves, and how they negotiate and reform their cultural identities. Elizabeth Cherry’s (2006) research on veganism as a cultural movement emphasizes the importance that a strong social network has on maintaining a vegan lifestyle. Finally, Mary Jane Collier’s (2015) article on identity and communication identifies norms, symbols, and meanings unique to the vegan culture and community. I hypothesize that ethical concerns are the main force behind adopting a vegan lifestyle. I want to further understand the role that community plays in forming a vegan identity, and, overall, to affirm that community is essential to maintaining, and thriving in, a vegan lifestyle. Vegan individuals, who are able to connect with other vegans, adhere more strictly to a plant-based diet. In comparison, vegans who do not partake in any social organizations or vegan networks are more likely to adapt the definition of veganism to fit their lifestyle. Community and networks play a considerable role in accountability, and they allow people not only to define themselves as vegan, but also permit others to identify as vegan, too.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".