Bibliographic record
Abstract
This article concerns the curious case of what I label the ‘litterbox lie’: a false narrative propagated on social media that litterboxes are being installed in grade school classrooms for kids who identify as cats. This lie, first circulated in Canada in 2021, has been raised by rightwing forces in response to the perceived accommodation of transgender children in public schools. This article deploys discursive and semiotic analysis to critically reconsider three entwined areas of ideological intrigue that inform this litterbox lie: cats, computers, crap. This terminological trinity contours the keywords of the litterbox lie – what I cheekily call its CATegories. I follow this conceptual round-up with an exploratory unravelling of the litterbox lie, and conclude with a ‘clawback’, as I answer the rightwing ridiculous with some radical ridicule of the queered cat creative kind, including the inter-species academe of trans studies stalwart Sandy Stone. Across its pages, this article re-presents the litterbox lie as a paradigm of present-day conservative conspiracy theories, one that pulls from persistent gender panic patterns and successful social media strategies of the cat kind. In a conclusion that refuses respectability, I offer some ‘catty’ examples of semiotic guerilla warfare as enabling trans-cat alternatives to this online anti-trans disinformation and hate.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.044 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".