Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it