The Animal-industrial Complex and the Politics of Resistance: A Critical Discourse Analysis of White Veganism
Bibliographic record
Abstract
Using the theoretical dimensions of critical animal studies (CAS) and the methodological field of critical discourse analysis (CDA), this project examined how the logic that produced and maintains the current system of animal agriculture in Canada and the United States is re-inscribed and/or challenged in online vegan advocacy. Data were collected from 10 online Facebook pages dedicated to vegan advocacy and included entries posted by the page administrator(s) within a 6-month period, as well as the top 5 user comments left on each of these entries. Across all 10 vegan advocacy groups, the total number of posts analyzed was 3,071 and the total number of user comments analyzed was 8,313. Overall, the analysis highlighted the ways in which White privilege and capitalist logic are reproduced and/or contested in online spaces of vegan advocacy. More specifically, CDA revealed the following themes in the data: commodity fetishism and “removing the veil”; conceptualization of animals; varying socio-political context; and relationship to other social justice causes. Drawing on these four themes, this project provides insight into the possibilities and limitations of vegan advocacy and emphasizes strategies for building solidarity across various social justice movements.
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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.015 | 0.048 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".