Greedy Bat Eaters versus Cruel Pig Killers: The Lose-Lose Battle of Divisive Discourse
Bibliographic record
Abstract
Unsurprisingly, the circumstances and challenges brought about by the COVID-19 pandemic have generated strong reactions. Among the more notable, Canadian musician and animal activist Bryan Adams made headlines when he went on a tirade on social media denouncing ‘fucking bat eating, wet market animal selling, virus making greedy bastards’ and advocating for veganism. This article uses this incident as a prism through which to examine the values and assumptions informing some of the central debates within the mainstream animal advocacy movement today. Certainly, there is an urgent need for a critical re-evaluation of the policies and practices that have created the conditions in which viral pathogens can spread, especially those relating to our treatment of nonhuman animals (and our relationship with nature more broadly). However, the roots of the problem are fundamentally structural, and not attributable to any one country or culture. The thoughtless use of terms that contribute to a politically charged and rancorous public debate readily descends into a lose-lose battle, which may hinder efforts to address complex and collective concerns in a mutually cooperative manner. If COVID-19 is to represent a turning point towards building a more equitable, sustainable, and resilient world for humans and nonhuman animals alike, the kind of fractioning that is currently being exacerbated by the use of divisive discourse must be eschewed in favour of a greater recognition of our fundamental interconnectedness, including through a more pluralistic understanding of law.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".