Tricky Ticks and Vegan Quips: The Lone Star Tick and Logics of Debility
Bibliographic record
Abstract
In this article, we explore the discourse around the Lone Star tick, predominately through the platform of Twitter, in order to highlight the way the tick is imagined as a potential tool for increasing veganism, as the Lone Star tick’s bite has been found to cause allergies to a carbohydrate found in red meat. In particular, the article questions why the notion of tick-as- vegan-technology is so widespread and easily called forward. In order to explain this pattern, we turn to Sunaura Taylor’s monograph, Beasts of Burden and Jasbir Puar’s notion of debility. Taylor’s monograph provides a framework for analyzing the imbrications of power between ableism and speciesism. Puar’s debility helps articulate how the imagination of widespread red meat allergies is an imagination of decapacitation. Puar’s analysis of the invisibilizing of debility also helps reveal how both ticks and humans are debilitated and instrumentalized in this articulated fantasy. We argue that the governance impulse in these discourses reflect a continued alignment with biopolitical forces that always designate some lives as worthy of care and others as useable, which is fundamentally at odds with broader goals of animal liberation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.056 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".