Bibliographic record
Abstract
Fables consists of 45 photographs taken across heritage and museum sites in France, collected in the book Fables Karen Knorr (2008). These large-format lamda prints and photographs have also been presented in 17 solo exhibitions and over 25 group exhibitions in France, Italy, Belgium, Netherlands, Canada, India and the UK. The context for the work is large-format staged or constructed fine art photographic practice (e.g. the work of Crewdson, Wall, Sherman, Sugimoto, Hunter and others). \n \nThe project evolved out of my work from the 1980s to 2000s, which challenged the institutional spaces of the museum and academy as sites of power through humour, irony and storytelling. The current work explores a new type of photographic imagining, using recent technological developments in digital photography: these make possible the digital insertion of the animal into the image as a trangressive device, creating a new hybrid object between photography, drawing and painting. \n \nMy aim is to expand photography as an allegorical form using the fable as a thematic and playful device: digital technologies are used to create a new photographic aesthetic that is able to develop institutional critiques in an accessible and entertaining way. At the same time the work involves a critique of the photograph as document: by combining analogue and digital photography, it troubles the ontological status of the photographic image. \n \nThe research crosses the disciplines of museology, aesthetics, feminism and animal studies in its investigation of the concept of animality, museology and natural history. It uses visual means to raise questions about the 'other' and alterity. The animal appears as a transgressive cipher (referencing surrealism) disrupting the order of things in locations of cultural power and challenging assumptions of cultural and national identity in European museums and heritage sites.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.274 | 0.077 |
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".