Bibliographic record
Abstract
Catopia begins from a simple feeling: images move fast online, but care happens in person. I partnered with the Regional Animal Protection Society (RAPS) Cat Sanctuary in Richmond, BC — a no-euthanasia refuge with aging pens and real daily constraints — to explore how design can borrow the language of the camera without turning animals into content. The project treats five camera foundations as spatial tools and prototypes them as small, retrofittable rooms that prioritise feline welfare: the Frame Pen invites visitors to wait while cats choose the scene, with a peephole wall that lets cats “frame” people; the Aperture Pen is a quiet chroma-green landscape that supports nap-anywhere behaviour and makes the ease of context erasure visible; the Dark Pen is exposed for cats, not humans; low light, soft acoustics, and a grove of scratching columns that slow bodies; the Glass Pen places a habitat behind a double-skin gallery where pixelated translucent glass turns details into silhouettes so gestures, not grabs, guide interaction; and the Filter Pen uses a coloured glass vault and perforated underlay to translate colour grading into gentle daily cues while offering moving “stars” to chase without lasers. Across pens, a concise feline brief pairs with an operations baseline and an ethics note that centers care over content. More than photogenic sets, Catopia offers a low-cost kit of parts others can tune : turning scrolling habits into spatial cues that slow us down, honour cats as agents, and make room for attention, consent, and everyday care.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.472 | 0.223 |
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".